phd in data science iisc bangalore

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Ph.D Computational and Data Sciences at IISc Bangalore Offline Course

Course details.

Doctor of Philosophy (Ph.D.) in Computational and Data Sciences is an offered Full-time three/six-year Doctoral program and it is offered by the Indian Institute of Science, Bangalore. Ph.D. programme shall be for a minimum duration of three years, including course work and a maximum of six years. Extension beyond the above limits will be governed by the relevant clauses as stipulated in the Statute/Ordinance of the individual Institution concerned.

Eligibility Criteria

BE / B Tech or equivalent degree in any discipline or M Sc or equivalent degree in Mathematical Sciences, Physical Sciences, Electronics, Instrumenta-tion, Computer Science or Master’s in Computer Application or Graduates of 4-year Bachelor of Science programmes. In all cases, a background in Mathematics and Programming is required.

Admission Details

For a Ph D programme in the Science Faculty candidates with ME /M Tech / M Sc(Engg) / MS / Master’s degree in Pharmacy, Agriculture, or Veterinary Medicine will be short-listed for an interview for Ph D programmes based on their academic performance (including their past performance in the national tests like GATE/ JRF/ GPAT in  which they have qualified prior to their Master’s degree).

Other Popular Courses in this College

Other popular universities offering this course, pes university, bangalore.

• Courses & Fees

International Institute of Information Technology Bangalore

National institute of technology karnataka surathkal, jss science and technology university, mysuru, visvesvaraya technological university, belagavi, popular certification, ibm data topology.

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Data Scientist Career Guide and Interview Preparation

What is data science, know more about this college, applications for admissions are open..

Amity University, Noida B.Tech Admissions 2024

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Ranked #52 among universities in India by NIRF | Highest CTC 50 LPA | 100% Placements | Last Date to Apply: 31st May

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100% Placement Record | Highest CTC 42 LPA | NAAC A++ Accredited | Ranked #68 in India by NIRF Ranking 2023 | Last Date to Apply: 17th May

Bharati Vidyapeeth University M.Tech Admissions

NAAC A+ Grade | 23000+ students | 1600+ Teachers

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Merit Scholarships | NAAC A+ Accredited | Top Recruiters : E&Y, CYENT, Nvidia, CISCO, Genpact, Amazon & many more

Pimpri Chinchwad University B.Tech Admissions

1000+ Recruiters | 450+ Patents | 50000+ Alumni network

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Certifications

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Scientific Machine Learning and Operations (STARS)

Department of Computational and Data Sciences, IISc, Bangalore

Scientific Machine Learning and Operations (STARS) at the Department of Computational and Data Sciences is under the Division of Interdisciplinary Research at Indian Institute of Science  Bangalore, India.

We develops accurate, efficient and robust parallel numerical (finite element) schemes for solving partial differential equations (PDEs),  Scientific Machine Learning and High Performance Computing.

Please visit our in-house finite element package  ParMooN .

STARS (Scientific Machine Learning and Operations)

STARS (Scientific Machine Learning and Operations) is a research group at the forefront of scientific computing, machine learning, and high-performance computing. Our primary focus revolves around two pivotal areas: Scientific Machine Learning (SciML) and Machine Learning Operations (MLOps). Within SciML, we are pioneers in developing innovative machine learning algorithms to solve complex closure problems, harnessing the power of Physics-informed neural networks (PINNs) to augment numerical schemes, and utilizing AI-based techniques to estimate uncertain model parameters. Additionally, we leverage data-driven approaches to revolutionize traditional scientific simulations, enhancing their accuracy and efficiency.

In parallel, our emphasis on MLOps drives us to push the boundaries of scalable ML algorithms and distributed training techniques. We delve into the realm of cloud computing to enable seamless deployment, ensuring ML models can handle large-scale datasets effortlessly. With an unwavering commitment to robustness and efficiency, we establish ML model and data version control, design and implement CI/CD pipelines, and streamline the entire ML deployment and operations process. By placing a strong focus on MLOps, we empower organizations to effectively integrate machine learning into their workflows, facilitating real-world applications and tangible impact.

While SciML and MLOps form the crux of our research, we also excel in other areas such as Data Science, where we pioneer anomaly detection, predictive behavior modeling, and personalized recommender systems with a focus on fintech applications. Additionally, our expertise in Computational Science extends to finite element analysis, multiphase flows, fluid-structure interactions, turbulent flows, and epidemiological events, employing hybrid CPU-GPU parallel algorithms and hardware-aware scalable parallel implementations.

In summary, STARS is a leading research group dedicated to advancing scientific computing, machine learning, and high-performance computing. Our unwavering focus on Scientific Machine Learning (SciML) and Machine Learning Operations (MLOps) positions us at the forefront of cutting-edge research, driving innovation and delivering practical solutions to complex problems in academia and industry.

• AI and MLOps for Defence

Finite Element Methods

• Finite element methods for the solution of PDEs and surface PDEs
• Multigrid methods, ALE approach for moving meshes
• Variational multiscale methods based Turbulence modelling for fluid flows
• Higher order discontinuous Galerkin schemes for hyperbolic PDE’s
• Fluid-structure interaction problems, ship hydrodynamics
• Turbulent flows with moving/deforming solid bodies

High Performance Computing

• ParMooN, an open-source finite element software development
• GPU-Accelerated Algebraic Multigrid solvers

Scientific ML

• Computational models for particle deposition in human air pathway
• Biophysical model of cancer invasion
• ML based stabilisation schemes for Singularly perturbed PDE’s
• Physics Informed Neural Networks for Fluid flows
• Reduced order modelling for fluid flows

M.Tech. in Data Science and Business Analytics

A Professional Online Masters' Program at IISc

Welcome to the dsba program, an online m.tech. program for working professionals and future leaders.

About the DSBA Program

The Data Science and Business Analytics (DSBA) is an online Masters’ program offered by the Departments of Computational and Data Science and Management Studies at IISc. This program is designed for working professionals to earn a full-fledged M.Tech. Degree from IISc, the top university in India, through an online instruction format that is on par with the residential M.Tech. program. Students have the flexibility of completing 8-10 courses and a capstone project over a 2-4 year period to earn their degree. All courses are taught by full-time faculty at IISc, renowned for their world-class research and development, which permeates into the lectures and practical sessions. A unique feature of the program is its blend of data science concepts both from a technology and a business perspective, that helps to train future technology leaders in data and AI driven industries.

Unlike other programs, admission to the online M.Tech. program at IISc is through a partnership with your parent company (sponsoring institution) and IISc. Applicants from the company are nominated and go through an admissions interview at IISc that evaluates their ability to be successful in the program. For more details, see the Admissions section.

Program Features

Future-proof your Career

A Masters' degree from IISc prepares you for future leadership, and even a subsequent Ph.D.

Upskill on AI/ML Methods, Technology & Business Domains

Learn cutting edge data science methods and data engineering at scale, with application to diverse business domains

Blends Theory with Practice

Establish rigorous foundations coupled with hands-on practical applications

Capstone Project in your Industry

Validate your learning through a Capstone Project relevant to your specific business or technology domain

Learn from the Leaders

Full-time IISc faculty teaching the program are reknown researchers and practitioners

Collaborate with Best-in-Class Peer Group

Our selective admissions through industry nominations and small class sizes allow you to network and collaborate with peers

Work-Life-Study Balance

The self-paced study offers 3 years for completion, with additional break in study for business and life events

Sponsored by your Company

Admissions are through nomination by your parent company and fees are paid by them too. Be rewarded for being an out-performer!

Program Structure

Students admitted to the M.Tech.(Online) program on Data Science and Business Analytics need to complete the following requirements within a 2-4 year period to earn their degree. Students must get a passing grade in a course for it to be counted toward their degree. More details are under Program Details and Courses .

Core courses

These three courses of 4 credits each have to be mandatorily taken and completed by all students in the program. These are typically taken in the first and second semesters.

• Introduction to Data Science
• Data Engineering at Scale
• Introduction to Computing for AI/ML

Elective courses

Students have to complete at least 20 credits of elective courses. Each course typically is for 3-4 credits. See the course details below. These courses can be taken at any time during the program, as long as the student meets its prerequisites. Not all electives are offered in all years, and others may be added over time.

• Financial Analytics
• Practical Machine Learning
• Data Mining
• Operations Research
• High-performance Computing in Data Science
• Deep Learning and Computer Vision

Capstone Project

A capstone project for 32 credits has to be completed over two terms, and can be started after completing all the core course requirements. The project topic will be proposed by the student, with relevance to their professional work, and utilizing skills gained during the degree program. A faculty mentor will offer high-level feedback on the project. There will be a midterm evaluation at the end of the first term and a final review after the second term, by a committee appointed by the Program Curriculum Committee (PCC).

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Integer cursus bibendum augue ac cursus .

The faculty managing the DSBA program and offering courses are drawn from the Division of Interdisciplinary Sciences at IISc, and specifically from the Departments of Computational and Data Science and the Departments of Management Studies . These are the same faculty who teach the other flagship post-graduate courses (M.Tech. in Computational and Data Science, and Master of Management) for full-time students at these departments. The faculty members bring the same world class expertise to the courses offered as part of the DSBA program.

The Program Curriculum Committee (PCC) is responsible for all academic matters of the program. The following are its members.

Associate Professor and Chair, Computational and Data Sciences

Pcc chair, dsba, sashikumaar ganesan.

Professor and Chair, Management Studies

Parthasarathy ramachandran.

Associate Professor, Computational and Data Sciences

Yogesh simmhan.

Assistant Professor, Computational and Data Sciences

Deepak subramani.

Assistant Professor, Management Studies

Shashi jain.

According to BA Times and US Bureau of Labor Statistics a Management Analyst with a Bachelor’s degree has an expected salary of US$85,000 per year, while with a Master’s degree, an Information Scientist expects US$122,000 per year and an Analytics Manager expects US$160,000 per year.

Kimberly Tran manager

Our program is targeted at professionals who have between 2-8 years of industry experience. This allows you to fully utilize the skills and challenges you have experienced at your work to draw the full potential from the program.

Unlike a purely technology-oriented data science, AI or machine learning degree, we blend the foundational and technological aspects of data science with their practical relevance to business settings. This equips our graduates with skills required for being technology leaders as well as business leaders.

The same experts who teach students on our full-time post-graduate students on-campus at IISc will handle the courses of DSBA. In fact, many of the online courses draw upon existing course content for our full-time programs.

Latest posts

IISc Launches Online M.Tech. Program

Information Session for Potential Candidates

DSBA program starts the first year with 50+ admissions

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Indian Institute of Science Data Science and Business Analytics Program, CDS219, Bangalore 560012 [Map]

[email protected]

+91 (80) 2293 2789

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The International School for Advanced Studies (SISSA) was founded in 1978 and was the first institution in Italy to promote post-graduate courses leading to a Doctor Philosophiae (or PhD) degree. A centre of excellence among Italian and international universities, the school has around 65 teachers, 100 post docs and 245 PhD students, and is located in Trieste, in a campus of more than 10 hectares with wonderful views over the Gulf of Trieste.

SISSA hosts a very high-ranking, large and multidisciplinary scientific research output. The scientific papers produced by its researchers are published in high impact factor, well-known international journals, and in many cases in the world's most prestigious scientific journals such as Nature and Science. Over 900 students have so far started their careers in the field of mathematics, physics and neuroscience research at SISSA.

Calibration requirement for Epoch of Reionization 21-cm signal observation. Part III. Bias and variance in uGMRT ELAIS-N1 field power spectrum

Saikat Gayen 1 , Rashmi Sagar 2 , Sarvesh Mangla 3,2 , Prasun Dutta 1 , Nirupam Roy 4 , Arnab Chakraborty 5 , Jais Kumar 6 , Abhirup Datta 2 and Samir Choudhuri 7

Published 13 May 2024 • © 2024 IOP Publishing Ltd and Sissa Medialab Journal of Cosmology and Astroparticle Physics , Volume 2024 , May 2024 Citation Saikat Gayen et al JCAP05(2024)068 DOI 10.1088/1475-7516/2024/05/068

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Author affiliations

1 Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi — 221005, India

2 Department of Astronomy, Astrophysics and Space Engineering, Indian Institute of Technology, Indore, Madhya Pradesh, 453552, India

3 National Centre for Radio Astrophysics, Tata Institute of Fundamental Research, Pune 411007, India

4 Department of Physics, Indian Institute of Science, Bangalore 560012, India

5 Department of Physics and McGill Space Institute, McGill University, Montreal, QC, H3A 2T8, Canada

6 Department of Physics, K. N. Government P. G. College, Gyanpur, Bhadohi — 221304, India

7 Centre for Strings, Gravitation and Cosmology, Department of Physics, Indian Institute of Technology, Madras, Chennai 600036, India

Prasun Dutta https://orcid.org/0000-0002-1122-6270

• Received 1 March 2024
• Accepted 15 April 2024
• Published 13 May 2024

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Power spectrum of H  i 21-cm radiation is one of the promising probes to study large scale structure of the universe and understand galaxy formation and evolution. The presence of foregrounds, that are orders of magnitude larger in the same frequency range of the redshifted 21-cm signal has been one of the largest observational challenges. The foreground contamination also hinders the calibration procedures and introduces residual calibration errors in the interferometric data. It has been shown that the calibration errors can introduce bias in the 21-cm power spectrum estimates and introduce additional systematics. In this work, we assess the efficacy of 21-cm power spectrum estimation for the uGMRT Band-3 observations of the ELAIS-N1 field. We first evaluate the statistics of the residual gain errors and perform additional flagging based on these statistics. We then use an analytical method to estimate the bias and variance in the power spectrum. We found that (a) the additional flagging based on calibration accuracy help reduce the bias and systematics in the power spectrum, (b) the majority of the systematics at the lower angular scales, ℓ< 6000, are due to the residual gain errors, (c) for the uGMRT baseline configuration and system parameters, the standard deviation is always higher than the bias in the power spectrum estimates. Based on our analysis we observe that for an angular multipole of ℓ∼3000, 2000 hours of `on source time' is required with the uGMRT to detect redshifted 21-cm signal at 3-σ significance from a redshift of 2.55. In this work we only consider the power spectrum measurement in the plane of the sky, an assessment of residual gain statistics and its effect on multifrequency angular power spectrum estimation for the uGMRT and the SKA like telescopes will be presented in a companion paper.

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