phd machine learning finland

46 machine-learning-phd positions in Finland

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PhD Student Position in Reinforcement Learning and Planning of High Performance

related to staff position within a Research Infrastructure? No Offer Description PhD Student Position in Reinforcement Learning and Planning of High Performance Neural Network Architectures The Robot

Project Researcher, Bioinformatics

well as to implement innovative machine learning , data integrative and multimodal solutions. The hosting core facility is located in the School of Medicine, Faculty of Health Sciences, on the UEF Kuopio campus

Call for Doctoral Students in Artificial Intelligence

machine learning research and in five application areas. Come do a PhD tackling challenging research questions in a network that fosters industry and multidisciplinary collaboration! The PhD students

Doctoral Researchers in Robotics, Autonomy, Control, and Machine Perception

position within a Research Infrastructure? No Offer Description Intelligent Work Machines (IWM) Doctoral Program is looking for PhD students (Doctoral Researchers). The IWM doctoral program educates new

Intelligent Work Machines doctoral program

? Not funded by an EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Digitalization, electrification, and autonomy make the machine industry to be

Doctoral education pilot / Seven (7) Doctoral Researchers in Finnish Doctoral Program Network in Artificial Intelligence (AI-DOC)

human modeling, Machine Learning , Statistics. Supervisors: Matti Vihola , Juha Karvanen , Jenni Raitoharju , Ilkka Pölönen , Kaisa Miettinen , Samuli Pekkola , Mikko Salo , Sara Taskinen , Klaus

PhD in 3 years: Six Doctoral Researchers in Intelligent Work Machines Pilot

robotics, mechatronics, machine design, machine dynamics, control theory, software technologies, real-time computing, human- machine interaction, communication, vehicle engineering, machine learning and

1-2 Doctoral Researchers to Intelligent Work Machines field

dynamics, control theory, software technologies, real-time computing, human- machine interaction, communication, vehicle engineering, machine learning , robotic learning , and business models related

Doctoral Researcher ( PhD student), the Nordic Borealization Network (NordBorN)

skills in remote sensing and machine learning . The position entails both office work and field work. We welcome candidates with a background in geography, ecology, environmental sciences, geoinformatics

1-2 Doctoral Researchers (Music Research)

of music research: embodiment, interaction, emotions, well-being, and self-regulation. Methodological approaches focus on experimental research, signal processing, machine learning , psychometrics, and

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The ELLIS unit Helsinki builds on the long tradition and track record of pioneering machine learning research in Finland and seeks to contribute to a concerted European effort in basic research in machine learning. In particular, the unit focuses on (1) Probabilistic modeling and Bayesian inference, (2) Simulator-based inference, (3) Data-efficient deep learning, (4) Privacy-preserving machine learning and (5) Interactive artificial intelligence. The faculty and the operations of the ELLIS unit Helsinki has close links to the Finnish Center for Artificial Intelligence (FCAI) which is a nation-wide center for AI, combining fundamental AI research with a broad range of applied AI research. The ELLIS unit Helsinki will support the FCAI mission to create a new type of AI, which is able to operate with humans in the complex world - and to renew industry.

Unit Director

Unit Members

Coordination

Unit Website

ELLIS Newsletter

If you want to receive the ELLIS newsletter regularly via email, please subscribe here:

Doctoral defence of Mustafa Beter, MSc, 5 April 2024: Epigenetic drugs to modulate angiogenesis

Childhood sedentariness accelerates premature vascular damage, uef presents young researcher awards and the first-ever doctoral supervisor award, sustainability and circular economy morning coffee session, unite spring webinars 2024: round table discussion about the role of gamification, mustafa beter, msc: doctoral defence in molecular medicine, kuopio.

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School of Computing

School of Computing that is situated on both Joensuu and Kuopio campuses provides high-quality education in the areas of computer science, information technology and statistics. Internationality is part of everyday life at the School. We engage in meaningful and international co-operation aimed at developing the education offered at the School to meet the future needs of society and working life.

Our internationally acclaimed research focuses on artificial intelligence (AI), data science, and future technologies. AI and data mining have long been utilised in many fields, and data use and analysis are set to increase in the future.

The School of Computing currently offers an opportunity to complete an international double degree at the Master’s level. The opportunity of an international double degree is provided for doctoral studies as well. We also participate in the university’s Institute of Photonics network and currently offer two international Erasmus Mundus Joint Master’s Degree programmes in co-operation with the Department of Physics and Mathematics.

Bachelor's/Master's degrees awarded per year

Doctoral degrees awarded per year, international peer-reviewed scientific publications, news and events, university of eastern finland applies for the right to offer education in data engineering, doctoral defence of mzomwe yahya mazana, msc, 27.3.2024: mathematics teaching and learning activity model for blended instruction in tanzanian higher education, doctoral defence of annastasia shipepe, msc, 20.12.2023: a design study on educational robotics in namibia, cultural knowledge and digital skills for local companies, finnish learning analytics and artificial intelligence in education conference - flaiec 2024, 4th international conference for sustainable resource society - ics24.

Events archive

Computing has a wide range of applications. As a student, you can select the most interesting courses from among those offered at the School to build your own specialisation studies. During advanced studies, you will participate in courses related to the research carried out at the School on Computing.

Computing professionals are in high demand in working life. Our graduates find employment in, for example, the software industry, public administration, retail, teaching, and research. Teacher training in computing, on the other hand, provides qualifications to work as a primary, secondary or upper secondary school teacher, as well as the abilities to work as a trainer in working life. Experts in computing are also needed in high-tech research and development.

In addition to the international programmes taught in English, the school also offers degree programmes taught in Finnish.

New student orientation

Exchange studies

Learning community of students of the School of Computing

Student organisation Serveri ry (Kuopio)

Student organisation Skripti ry (Joensuu)

Alumni network

Continuous learning

Sustainable Technologies, Bachelor's and Master's Degree (in Finnish)

Computer Science, Bachelor's and Master's Degree (in Finnish)

Business Economics and Data Science, Bachelor's and Master's Degree (in Finnish)

International Master's degree programmes

Master's Programme in Information Technology (IMPIT)

Imaging and Light in Extended Reality (IMLEX) Erasmus Mundus Japan

Computational Colour and Spectral Imaging (COSI) Erasmus Mundus

Phd studies in the school of computing.

Doctoral-level education in the School of Computing offers doctoral students high level systematic research training and give a deep insight in to the specific research area chosen by the student. The research focus areas of the School are

Artificial Intelligence

Data science.

• Future technologies

We belong to Doctoral Programme in Science, Forestry and Technology.

PhD Studies in Computer Science

Doctoral Programme in Science, Forestry and Technology (LUMETO)

The three main areas of research at the School of Computing are: artificial intelligence, data science, and future technologies. We engage in close co-operation with our partners, and the research we do is respected internationally.

The focus areas of our AI research are computational intelligence and speech as well as computational spectral imaging and machine learning. Data science is one of the modern society’s key drivers of change in both research and business. Our research is aimed at developing new methods for data analysis. Statistical methods, on the other hand, are the key tools used by researchers in all fields of science that are based on empirical data.

Research and development in future technologies focus on new solutions for learning and development. Our society is digitalising rapidly, and new skills are constantly required to operate in it. Research in learning analytics is particularly working on pushing the boundaries of methodological innovations in learning analytics. Our research in interactive technologies focuses on eye-tracking, digital accessibility and human-computer interaction (HCI). 

Dissertations

Research communities

Search research group, project or expert in UEF Connect

Open science

Our research groups

Computational Intelligence

Computational Speech Group

Machine Learning

Computational Spectral Imaging

Algorithmic Data Analysis

Nature-Inspired Machine Learning

Statistics Education and Research

Future Technologies

Interactive Technologies

Learning Analytics

Technologies for Learning and Development

Contact information

Head and deputy heads.

Markku Tukiainen

Head of Department

School of Computing, Faculty of Science, Forestry and Technology

markku.​tukiainen​@uef.fi

+358504411508

Erkki Pesonen

University lecturer.

Deputy Head of Department

erkki.​t.​pesonen​@uef.fi

+358403552562

Simo Juvaste

simo.​juvaste​@uef.fi

+358503803416

Professors and associate professors

Mohamed Abdelgalil

Associate professor.

mohammed.​saqr​@uef.fi

+358503089369

Roman Bednarik

roman.​bednarik​@uef.fi

+358414306116

Pasi Fränti

pasi.​franti​@uef.fi

+358504422265

Xiaozhi Gao

xiao-​zhi.​gao​@uef.fi

+358505147512

Markku Hauta-Kasari

1st Vice-Dean

markku.​hauta-​kasari​@uef.fi

+358504056231

Ville Hautamäki

ville.​hautamaki​@uef.fi

+358504422350

Tomi Kinnunen

tomi.​kinnunen​@uef.fi

+358504422647

Pauli Miettinen

pauli.​miettinen​@uef.fi

+358504753210

Matti Tedre

matti.​tedre​@uef.fi

+358504340376

Pekka Toivanen

pekka.​toivanen​@uef.fi

+358405439021

Senior teaching and research personnel

Nestoras antoniou, postdoctoral researcher.

nestoras.​antoniou​@uef.fi

Mikko-Ville Apiola

mikko.​apiola​@uef.fi

+358504322670

Ivan Franco Gonzalez

ivan.​franco​@uef.fi

+358504710284

Senior Researcher

pauli.​falt​@uef.fi

+358505018677

Esther Galbrun

esther.​galbrun​@uef.fi

+358504729297

Keijo Haataja

Project manager.

keijo.​haataja​@uef.fi

+358505272359

Marko Hassinen

marko.​hassinen​@uef.fi

+358504778152

Virpi Hotti

Senior university lecturer.

virpi.​hotti​@uef.fi

+358405629192

mika.​hujo​@uef.fi

+358403553601

Joni Hyttinen

joni.​hyttinen​@uef.fi

+358505054232

Matti Itkonen

matti.​itkonen​@uef.fi

+358504797075

Ilkka Jormanainen

ilkka.​jormanainen​@uef.fi

+358503757289

Marko Jäntti

marko.​jantti​@uef.fi

+358504616738

Harri Karhu

University teacher.

harri.​karhu​@uef.fi

+358505251695

juho.​kopra​@uef.fi

+358505689101

Gaetano La Russa

gaetano.​la.​russa​@uef.fi

+358504731154

Niko Lappalainen

Project researcher.

niko.​lappalainen​@uef.fi

+358504797118

Sonsoles López Pernas

sonsoles.​lopez​@uef.fi

+358504126453

Matti Nykänen

matti.​nykanen​@uef.fi

+358403553521

Nicolas Pope

nicolas.​pope​@uef.fi

+358505288449

Ismaila Sanusi

ismaila.​sanusi​@uef.fi

+358504724677

Dmitri Semenov

Research manager.

dmitry.​semenov​@uef.fi

+358503043941

Jarkko Suhonen

Staff scientist.

jarkko.​suhonen​@uef.fi

+358504358927

Hana Vrzáková

hana.​vrzakova​@uef.fi

+358503267676

Samuel Yigzaw

samuel.​yigzaw​@uef.fi

PhD students

Sami Andberg

Doctoral researcher.

sami.​andberg​@uef.fi

Muhammad Afzal

Department of Chemistry, Faculty of Science, Forestry and Technology

muhammad.​afzal​@uef.fi

Ramy Elmoazen

ramy.​elmoazen​@uef.fi

+358504767176

Maiju Karjalainen

maiju.​karjalainen​@uef.fi

+358469203579

Salman Khalil

salman.​khalil​@uef.fi

+358503208916

Federico Malato

federico.​malato​@uef.fi

Andrius Penkauskas

andrius.​penkauskas​@uef.fi

+358504391219

Mohammadhossein Salari

mohammadhossein.​salari​@uef.fi

+358505626207

Hasnain Shah

ali.​shah​@uef.fi

+358504081364

Vishwanath Singh

vishwanath.​singh​@uef.fi

Project Coordinator

md.​sultan​@uef.fi

Amos Sunday

Visiting researcher.

amos.​sunday​@uef.fi

Jimi Tuononen

jimi.​tuononen​@uef.fi

+358503228402

janne.​tyni​@uef.fi

+358505717136

Administrative personnel

Marjut anttilainen, project controller.

Financial Services, University Services

marjut.​anttilainen​@uef.fi

+358504425631

Maunu Aunesluoma

maunu.​aunesluoma​@uef.fi

Aaya Bougrine

aaya.​bougrine​@uef.fi

Manasi Chhibber

Research assistant.

manasi.​chhibber​@uef.fi

Juha Hakkarainen

Laboratory engineer.

Digital Services, University Services

juha.​hakkarainen​@uef.fi

+358503726261

Laura Hurmalainen

Coordinator.

laura.​hurmalainen​@uef.fi

Jesper Kauppinen

Teaching assistant.

jesper.​kauppinen​@uef.fi

Oili Kohonen

Training officer.

Student and Learning Services, University Services

oili.​kohonen​@uef.fi

+358503789363

Maria Kuismin

Research trainee.

School of Humanities, Philosophical Faculty

maria.​kuismin​@uef.fi

Veera Kukkola

Study secretary.

veera.​kukkola​@uef.fi

+358503434139

Jussi Kukkonen

jussi.​kukkonen1​@uef.fi

Janne Laakkonen

janne.​laakkonen​@uef.fi

Merja Leppänen

merja.​leppanen​@uef.fi

+358403552262

Veikko Miettinen

veikko.​miettinen​@uef.fi

+358504360130

Paavo Pakoma

paavo.​pakoma​@uef.fi

+358403552570

Jukka Pitkänen

It specialist.

jukka.​pitkanen​@uef.fi

+358404863424

Sofia Purontaus

sofia.​purontaus​@uef.fi

Olli Tiilikainen

olli.​tiilikainen​@uef.fi

+358505210138

Laura Toivanen

laura.​toivanen​@uef.fi

+358503135381

Miika Turja

miika.​turja​@uef.fi

Student and Learning Services

University of Eastern Finland’s Student and Learning Services is responsible for providing general study-related administrative services for students and staff, as well as offer support for applicants.

Read more:  Student and Learning Services

Joensuu Campus

Postal address

University of Eastern Finland

P.O. Box 111

FI-80101 Joensuu

Street address

Länsikatu 15

Joensuu Science Park, 3rd floor

Kuopio Campus

P.O. Box 1627

FI-70211 Kuopio

Microkatu 1

KPY Novapolis building, F and G wings, 2nd floor  

FINLAND: Several Openings for Postdocs and PhD students in Machine Learning

Finnish Center for Artificial Intelligence FCAI and ELLIS Unit Helsinki invite applications for research positions in machine learning. You will join one of the top AI research centers in the Nordics and in Europe, with an access to an excellent network of scientists and broad range of possibilities to work with companies.

We are looking for postdocs and PhD students to FCAI and ELLIS Unit Helsinki . Your research can be theoretical, applied, or both. The positions are in the following areas of research:

1) Reinforcement learning 2) Probabilistic methods 3) Simulation-based inference 4) Privacy-preserving machine learning 5) Collaborative AI and human modeling 6) Machine learning for science

You will join a community of machine learning researchers and will be part of a broader team of researchers studying similar topics, mentored by a group of several experienced professors. 

What we are looking for

You have previous experience in machine learning, statistics, artificial intelligence or a related field, preferably demonstrated by success in related studies (PhD student applicants) and/or publication record in the leading machine learning venues (e.g. AAAI, AISTATS, ICLR, ICML, JMLR, NeurIPS) (postdoc/research fellow applicants). Other merits demonstrating suitability for a researcher position can also be considered.

You hold (or expect to shortly receive) a Master’s degree (PhD student applicants) or a PhD (postdoc applicants) in computer science, statistics, electrical engineering, mathematics or a related field.

Experienced postdoc applicants can be considered for research fellow positions, typically having previously worked successfully as postdocs for several years.

The positions require the ability to work both independently and as part of a team in a highly collaborative and interdisciplinary environment.

1) Research environment

FCAI’s research mission is to create new types of AI that are data-efficient, trustworthy, and understandable. We work towards this by developing machine learning principles and methods, and by building AI systems capable of helping their users make better decisions and design sustainable solutions across a range of tasks from health applications to materials science. Examples of latest research results are highlighted, e.g., in NeurIPS 2023 conference.

You will join a community of machine learning researchers who all make important contributions to our common agenda, providing each other new ideas, complementary methods, and attractive case studies. Your research can be theoretical, applied, or both. You will be part of a broader team of researchers studying similar topics, mentored by a group of several experienced professors. Our community is fully international, and the working language is English.

Our research environment provides you with a broad range of possibilities to work with companies and academic partners, and supports your growth as a researcher. FCAI, host of ELLIS Unit Helsinki , is a salient part of the pan-European ELLIS network, which further strengthens our collaboration with other leading machine learning researchers in Europe. In addition, our local and national computational services give our researchers access to excellent computing facilities, spearheaded by the EuroHPC LUMI , one of the the fastest and greenest supercomputers in the world.

2) Job details

The positions are based either at Aalto University or at the University of Helsinki , depending on the primary supervisor. All positions are fully funded, and the salaries are based on the Finnish universities’ pay scale. The contract includes occupational healthcare. 

Postdoc positions are typically made for up to three years. Following the standard practice, the PhD student position contract will be made initially for two years, then extended to another two years after a successful mid-term progress review.

Starting dates are flexible. All positions are negotiated on an individual basis and may include, e.g., a relocation bonus, an independent travel budget or research software engineering support.

We are strongly committed to offering everyone an inclusive and non-discriminating working environment . We warmly welcome qualified candidates from all backgrounds to apply and particularly encourage applications from women and other groups underrepresented in the field. 

Areas of research

1) reinforcement learning.

We develop reinforcement learning techniques to enable interaction across multiple agents including AIs and humans. We also work on manifold applications, ranging from drug design to autonomous traffic. Examples of recent research include:

Training and evaluation of deep policies using reinforcement learning and generative models (JMLR 2022)

Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs (AAMAS 2022)

Precise atom manipulation through deep reinforcement learning (Nat. Comms. 2022)

Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets (NeurIPS 2023)

2) PROBABILISTIC METHODS

We develop AI tools using probabilistic programming, with our main expertise in Bayesian machine learning. The research is disseminated as modular open-source software, including software for the most popular probabilistic programming framework Stan. Examples of recent research include:

Generative modelling with inverse heat dissipation (ICLR 2023)

Memory-based dual Gaussian processes for sequential learning (ICML 2023)

Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming (Statistics and Computing 2023)

Prior knowledge elicitation: The past, present, and future (Bayesian Analysis 2023)

Lagrangian manifold Monte Carlo on Monge patches (AISTATS 2022)

Detecting and diagnosing prior and likelihood sensitivity (Statistics and Computing, accepted for publication) + priorsense package (software)

3) Simulation-based inference

We develop simulation-based methods to learn generative models from the data. Main initiatives include: (1) ELFI , a leading software platform for likelihood-free inference of interpretable simulator-based models and (2) numerous leading GAN-based technologies. Examples of recent results:

Learning Robust Statistics for Simulation-based Inference under Model Misspecification (NeurIPS 2023)

Visualization of extensive datasets (Statistics and Computing 2023; Phil. Trans 2022)

Alias-Free Generative Adversarial Networks (NeurIPS 2021) + StyleGANs (software)

Causal discovery for the microbiome (Lancet Microbe 2022)

ABC of the future (International Statistical Review 2022)

4) PRIVACY-preserving Machine LEARNING

We develop theory and methods for privacy-preserving machine learning using differential privacy. We focus especially on high-utility differentially private deep learning and differentially private synthetic data. Examples of recent research include: 

Practical Differentially Private Hyperparameter Tuning with Subsampling (NeurIPS 2023)

Noise-Aware Statistical Inference with Differentially Private Synthetic Data (AISTATS/PMLR 2023)

Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT (AISTATS/PMLR 2021)

On the Efficacy of Differentially Private Few-shot Image Classification (pre-print)

5) Collaborative AI and human modeling

We develop probabilistic methods and inference techniques for reinforcement and machine learning in assistance settings with realistic and interactive user models. These systems treat and model human users as active agents to reason over and collaborate with, instead of passive sources of data. Examples of recent research include: 

Towards machines that understand people (AI Magazine 2023)

AI-assisted design with human-in-the-loop (AI Magazine 2023)

Amortized inference with user simulations (ACM CHI 2023)

Zero-shot assistance in sequential decision problems (AAAI 2023)

Adapting user interfaces with deep model-based reinforcement learning (ACM CHI 2021)

6) Machine learning for science

Machine learning is increasingly being used as a key element in research in different fields. Our interest lies in the general question of how machine learning could be used as part of the research process, essentially to improve the results and the scientific process itself. We seek solutions that work across multiple disciplines and applications. The work relates closely to our Virtual Laboratories initiative. Examples of recent initiatives include:

Virtual Laboratories: Transforming research with AI (perspective piece, preprint)

Modular pipeline for design assistance (software platform (WIP))

Virtual Laboratory for Molecular Level Atmospheric Transformations Centre of Excellence ; publication example (virtual laboratory, center of excellence)

Engineering new enzymes with machine learning (research project)

Teaming up with The Alan Turing Institute to develop Virtual Laboratories (collaboration)

How to apply?

The deadline for the postdoc applications is on January 14 and for the PhD student applications on January 21, 2024 . Please send your application to our recruitment system, links below. 

This call is administered together with the Helsinki Institute for Information Technology (HIIT). You can find the details on how to apply below:

Postdoc and research fellow positions: APPLY HERE

In the application form you are asked to select

the research area(s) you are interested in; please select “Artificial Intelligence (AI) and Machine Learning (ML)” to apply for FCAI positions

the intended supervisor(s) you are interested in working with; please select “Finnish Center for Artificial Intelligence FCAI” to apply for FCAI positions

In your cover letter, please specify the FCAI Teams and supervisors with whom you want to work.

Doctoral researcher positions: APPLY HERE

the research area(s) you are interested in; please select “Artificial Intelligence and Machine Learning” to apply for FCAI positions

General FAQ for the doctoral student call: https://www.hiit.fi/ict-community-doctoral-researcher-positions/

Required attachments:

Cover letter (1–2 pages). Please specify the FCAI Teams and supervisors with whom you want to work.

List of publications (please do not attach full copies of publications)

A transcript of doctoral/MSc&BSc studies and the degree certificate of your latest degree. If you are applying for a postdoc position and don’t yet have a PhD degree or for a PhD student position and don’t have a Master's degree, a plan of completion must be submitted.

Contact details of two senior academics who can provide references. We will contact your referees if we need recommendation letters.

All materials should be submitted in English in a PDF format. Note: You can upload max. five files to the recruitment system, each max. 5MB.

Deadline: January 14

Finnish Center for Artificial Intelligence FCAI is an international research hub initiated by Aalto University , the University of Helsinki , and the Technical Research Centre of Finland VTT . We are part of ELLIS , the pan-European AI network of excellence, and we host ELLIS Unit Helsinki .

FCAI and ELLIS Unit Helsinki are built on a long track record of pioneering machine learning research. We create methods and tools for AI-assisted decision-making, design and modeling, and use them to renew industry and society. Currently, over 70 professors contribute to our research.

Our researchers have access to excellent computing facilities through local and national computational services, spearheaded by the EuroHPC supercomputer LUMI , the fastest supercomputer in Europe.

Our community organizes frequent seminars, e.g., ELLIS Distinguished Lectures , Machine Learning Coffee Seminar and Seminar on Advances in Probabilistic Machine Learning . We offer high-quality collaboration opportunities with other leading research networks and companies . For instance, FCAI has a joint research center with NVIDIA and Finnish IT Centre for Science CSC and collaborates closely with the Alan Turing Institute.

About Finland

Finland is a great place for living with or without family: it is a safe, politically stable, and well-organized Nordic society, where equality is highly valued and extensive social security supports people in all life situations.

Finland's free high-quality education system is also internationally renowned. Finland has been listed as the happiest country in the world for the fifth year running. Find more information about living in Finland here and here .

Finland’s universities are committed to promoting equality and inclusion and preventing discrimination, to ensure that all students and staff feel welcome at universities and that it is easy to come and study or work in Finland. The rector of the University of Helsinki and the president of Aalto University are clear about the importance of internationalization.

Machine Learning, Data Science and Artificial Intelligence - Computer, Communication and Information Sciences, Master of Science (Technology)

Application period:

Language of instruction:, eligibility:, field of study:, organising school:, tuition fees:, description.

What exactly is intelligence, and how does it evolve? What is learning and why has 'learning to learn' become a crucial skill in today’s world? Wanting to find answers to questions as plain and straightforward as these can be enough of a reason for someone to study machine learning, data science and artificial intelligence. Yet these fields also deal with some of the most challenging problems of the 21st century, making the Machine Learning, Data Science and Artificial Intelligence (or “Macadamia”) major at Aalto University an ideal study environment for someone who is motivated to get out of their comfort zone. Be it to find new solutions to tackle climate change or better understand the causes of an epidemic, artificial intelligence, data science and machine learning have an integral part to play. 

A Macadamia graduate: 

• is able to define data-intensive problems in data science and artificial intelligence and understand their underlying statistical and computational principles. 
• is able to evaluate the suitability of different machine learning methods for solving a new problem encountered in industry or academia, and apply the methods to the problem. 
• can effectively interpret the results of a machine learning algorithm, assess its credibility, and communicate the results to experts of other fields. 
• can implement common machine learning methods and design and implement novel algorithms by modifying the existing approaches. 
• understands the theoretical foundations of the machine learning field to the extent required to be able to follow research in the field. 
• understands the opportunities that machine learning offers in data science and artificial intelligence. 

Tuition fees and scholarships

Aalto University’s tuition fee for master’s programmes taught in English is 15 000 euros per academic year. Tuition fees apply to citizens of countries other than those of the European Union (EU), the European Economic Area (EEA) or Switzerland. 

Aalto University has a scholarship programme to support non-EU/EEA citizens who study in a fee-charging degree programme. The scholarship may cover 100% or 50% of the tuition fee. 

More information on tuition fees and scholarships at Aalto University is available at the Scholarships and Tuition Fees webpage. 

Structure of studies

Master’s Programme in Computer, Communication and Information Sciences – Machine Learning, Data Science and Artificial Intelligence comprises a total of 120 ECTS credits.

The two-year programme consists of: 

• Major studies (60 ECTS) 
• Elective studies (30 ECTS) 
• Master’s thesis (30 ECTS)

Specialisations

Aalto University’s Department of Computer Science is quickly rising in rankings and is now among the top departments in Europe. Students in the Machine Learning, Data Science and Artificial Intelligence major are provided with access to cutting edge research and guidance from leaders in the field. ​ 

The studies emphasise active, hands-on learning. Projects and different practical assignments are meant to engage students in active learning and encourage them to try out things for themselves instead of remaining passive recipients of information. The faculty consists of enthusiastic and internationally acclaimed professors and researchers in the field, all contributing to an enjoyable and encouraging learning environment. To give concrete examples of the courses available, the following is a selection from the programme’s extensive curriculum: 

• Machine Learning: Supervised Methods (5 ECTS) 
• Deep Learning (5 ECTS) 
• Bayesian Data Analysis (5 ECTS) 
• Machine Learning: Advanced Probabilistic Methods (5 ECTS) 
• Artificial Intelligence (5 ECTS) 

Major compulsory courses at the beginning of the studies provide a strong foundation before further study in specific sub-areas. Students have the opportunity to dive deeper into areas such as Bioinformatics and Speech and Language. There is also a range of general optional courses for students to choose from and they can also include optional courses from other majors in their study plan per agreement with a professor in charge of the major. 

More information on the programme content and curriculum can be found in the Student guide . There may be some changes to the courses for the academic years 2024–2026 — the new curricula will be published in April 2024, when they will also be visible in the Student guide . 

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Internationalisation

The study environment in the programme is strongly international and studies are conducted in multicultural groups. The School of Science offers diverse possibilities for student exchange and internships all over the world. Students may find themselves doing an internship in Silicon Valley or taking a summer course at one of Aalto's partner institutions. 

Macadamia students have also the opportunity to take their second-year studies at EURECOM, France, or Grenoble INP, France and complete a double degree graduating from both Aalto University and EURECOM or Grenoble INP. In addition, the Macadamia major co-operates closely with ELLIS , the European Laboratory for Learning and Intelligent Systems, which has some of the best academic institutions and scientists under its umbrella. Should the students want to become top researchers in the field, they have an excellent opportunity for that. 

Aalto University is international by nature, welcoming thousands of degree and exchange students from abroad every year. These students join the diverse Aalto community not only through their studies, but also through multiple free time events, celebrations and extracurricular activities around the campus. Active tutoring programs and support services work hard to help international students integrate to the Nordic culture and feel at home in Finland. 

Further study opportunities

The programme qualifies for doctoral studies (Doctor of Science in an applicable field). 

Career opportunities

Machine learning and artificial intelligence are disrupting virtually every business in every industry. Staying on top of this revolutionary technology is imperative for organisations seeking to maintain a competitive edge. 

Since the demand for AI professionals outpaces the current skilled AI engineers, the graduates of this major have limitless opportunities open for them, ranging from process industry to data science. Recent spearhead application areas include bioinformatics, computational astrophysics, biology, and medicine, interactive technologies, information retrieval, information visualisation, neuroinformatics, and social-network analysis. 

Typical entry-level job titles of recent graduates include 

• Analyst, Analytics Engineer 
• Data Analyst, Data Scientist 
• DevOps Engineer 
• Machine Learning Software Engineer 
• Software Developer 
• Software Engineer 

Graduates can expect to advance rapidly in their chosen career. 

Examples of companies our recently graduated alumni work for: Accenture, Aureus Analytics, Discover Financial Services, Futurice, Elsevier, Jongla, Omniata Inc, Reaktor, Sanoma, Silo AI and Verto Analytics. 

Our recently graduated alumni are PhD students in the following universities: Aalto University, Brown University, Carnegie Mellon University, French Institute for Research in Computer Science and Automation (Inria), Purdue University, Télécom Paris Tech, University of Bristol, University of California - Santa Cruz, University of Iowa, University of Surrey. 

Aalto University has well-established career services to support students’ employment in Finland and abroad. Thanks to the flexible curriculum, many Aalto students work already during their studies and guarantee themselves entry positions before graduation. There is also a very active entrepreneurship community at Aalto, working as a springboard for founding a company. 

School of Science graduates in working life

School of Science Master's graduates are highly successful in finding jobs corresponding to their education. On this page you can find information on job titles, job sectors and career development of our graduates.

Research focus

The studies in the programme are closely related to the world-class research conducted at the Department of Computer Science . The best Macadamia students are warmly welcome as doctoral students in Aalto University’s research groups. 

Co-operation with other parties

There is close collaboration in teaching and research between Aalto University and the University of Helsinki in the form of joint activities within the Finnish Center for Artificial Intelligence (FCAI). The latter brings together top talents in academia, industry and public sector to solve real-life problems using both existing and novel AI. One of the current research areas centres around the opportunities that AI creates for medicine. Excellent Macadamia students can continue their studies in the Helsinki Doctoral Education Network in Information and Communication Technology (HICT). 

Students can also include multidisciplinary studies to their degree by studying a minor or optional courses from other fields. They also have the option to take courses from other Finnish universities via The Flexible Study Right Agreement (JOO). 

Aalto University is well-known for bridging disciplines of business, arts, technology and science. The lively campus and freedom of choosing elective courses across the University bring students from different fields under one roof. This spontaneous multidisciplinary environment sparks new ideas, gathers enthusiasts around them and gives birth to friendships, networks, and every so often, startups. 

Programme-specific admission requirements

Applicants to Machine Learning, Data Science and Artificial Intelligence (Macadamia) meeting the general eligibility criteria for master's studies are evaluated according to the below Evaluation criteria based on their admission group . The evaluation process is described under Applicant evaluation process . In addition to the obligatory application documents , this study option asks the applicants to submit also the documents listed under Requested documents .

The degree that gives the applicant the eligibility to apply (i.e. bachelor's degree) determines which admission group the applicant belongs to even in cases where the applicant has more than one higher education degree.

Admission group 1

Bachelor’s degree from a university or university of applied sciences in Finland

Evaluation criteria

Macadamia applications in Admission group 1 are evaluated based on the following criteria:

• Academic performance
• Relevance of previous studies
• Recognition and quality of institution
• Suitability
• Other areas of competence

Applicant evaluation process

During the evaluation of eligible applications, Macadamia applications in Admission group 1 are first evaluated based on the following criteria:

Only the applications who fulfil the requirements for these criteria will be evaluated against the full set of evaluation criteria . It is not possible to compensate for these criteria with other criteria. This means, for example, that motivation for Master level studies in this subject does not compensate for low grades or that relevant work experience does not compensate for higher education studies in the required subjects.

After the evaluation of the remaining criteria below, the best applicants will be selected based on the joint evaluation of all criteria.

The admission process is very competitive and only the best applicants are selected yearly. Not all applicants fulfilling the requirements can be admitted.

Requested documents

In addition to obligatory application documents, the Macadamia applicants in Admission group 1 are requested to provide the following programme-specific documents:

• relevant experience and achievements,
• expectations and motivation for MSc studies majoring in Machine Learning, Data Science and Artificial Intelligence at Aalto University, and
• future career aspirations and how MSc studies in Machine Learning, Data Science and Artificial Intelligence contribute to them.
• The motivation letter should be written in English. The recommended length is one page (font size 11 pt).
• Curriculum Vitae (CV)*

*) The lack of this document will cause rejection.

In addition, these additional documents add value to your application:

• The recommendation letter should preferably be from a university professor, lecturer or a thesis instructor who has supervised the applicant’s studies. There are no specific instructions for the contents of the recommendation letter. The letter should comment on the applicant’s suitability and aptitude for the programme. Recommendation letters written by work supervisors are accepted as well in case some time has passed since graduation.
• Short course descriptions of courses taken in the relevant subject areas
• Work certificates and other certificates of relevant achievements
• Copies of any publications
• Official transcript of records for other university studies which are not included in the mandatory part of the application (e.g. incomplete degrees, exchange studies, non‐degree studies)

Admission group 2

Bachelor’s degree from a higher education institution abroad

Macadamia applications in Admission group 2 are evaluated based on the following criteria:

• Standardized tests

During the evaluation of eligible applications, Macadamia applications in Admission group 2 are first evaluated based on the following criteria:

In addition to obligatory application documents, the Macadamia applicants in Admission group 2 are requested to provide the following programme-specific documents:

• The accepted exams are the GRE General Test and GRE Revised General Test. At home tests are accepted. The GRE Subject Test is not acceptable. See the homepage of GRE for more information.
• The official test results must arrive at Aalto University directly from the test administrator no later than 9 January 2024. The GRE test reporting code for Aalto University is 7364 . Please include also a scanned copy of the test result in your application documents.
• GRE scores are valid for five years after the test day. For 2024 admissions, the oldest acceptable test day is 30 November 2018.

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Department of Computer Science

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Contact information

Learning Services at Aalto University School of Science

For enquiries regarding the programme-specific application documents or studies in the programme, please contact Learning Services of Aalto University School of Science

[email protected]

Admission Services

For enquiries regarding the application process, obligatory application documents or English language proficiency, please contact Admission Services

• Published: 6.9.2018
• Updated: 27.11.2023

Postdoc, Research fellow, and PhD student positions in Machine learning and AI (Helsinki, Finland; deadline Oct 1)

Sep 12, 2023

We are hiring in Helsinki Finland, to my research group and in the Finnish Centre for Artificial Intelligence AI and ELLIS Unit Helsinki. These are two separate calls – you can apply in one or both calls:

1. My research group, probabilistic machine learning: https://aalto.wd3.myworkdayjobs.com/aalto/job/Otaniemi-Espoo-Finland/Postdoctoral-and-doctoral-researcher-positions-in-Probabilistic-Machine-Learning-research-group–Aalto-University_R37227-3

2. Finnish Center for Artificial Intelligence and ELLIS Unit Helsinki: https://fcai.fi/we-are-hiring

Keywords in both : 1) Reinforcement learning, 2) Probabilistic methods, 3) Simulation-based inference, 4) Privacy-preserving machine learning, 5) Collaborative AI and human modeling, 6) Machine learning for science, and 7) differential privacy.

The positions can include a suitable combination of theoretical and methodological work, and applications such as drug design, synthetic biology, economics, neuroimaging

FAQ: Q: What is the difference; how do I know which call and topic to apply in? A: Pick a topic that is most fitting to you, and tell more about your wishes in the cover letter.

Deadline is October 1, 2023

More information: Aalto Probabilistic Machine Learning Group, Finnish Center for Artificial Intelligence FCAI and ELLIS Unit Helsinki

Doctoral programmes