PhD in

this course provides a broad overview of bioinformatics for single cell omics technologies, a new and fast-growing family of biological assays that enables measuring the molecular contents of individual cells with very high resolution and is key to advancing precision medicine. The course starts with an accessible introduction to basic molecular biology: the cell structure, the central dogma of molecular biology, the flow of biological information in the cell, the different types of molecules in the cell, and how we can measure them. This course then introduces students to the diverse landscape of biological data, including its types and characteristics and explores the foundational principles of single-cell omics bioinformatics, encompassing key methodologies, tools, and computational workflows, with an emphasis on the development of foundation models for single cell omics data

This interdisciplinary course is designed for students and professionals with a background in machine learning who seek to understand the fundamentals of molecular biology. The course explores key biological concepts and their applications to machine learning, particularly in the fields of bioinformatics, genomics, and computational biology. By bridging the gap between molecular biology and machine learning, students will gain the knowledge to apply machine learning techniques to biological data and solve complex problems in medicine, genetics, and drug discovery.

Computational biology has become an important discipline in the intersection of computing, mathematics, biology and medicine. Biological data sets produced by modern biotechnologies are large and hence they can only be understood by using mathematical modeling and computational techniques. Starting from analysis of genetic sequences, the field has progressed towards analysis and modeling of entire biological systems. A way of abstracting the vast amount of biomedical information is by modeling and analyzing these data by using networks (or graphs). Such approaches have been used to model phenomena in other research domains, apart from computational and systems biology and medicine.

This interdisciplinary graduate course introduces students to the core concepts, tools, and emerging methods in computational genomics and epigenomics. It is designed for students from biology, computer science, machine learning, and related fields who are interested in applying their skills to cutting-edge questions in biology and medicine. Students will explore how genomic and epigenomic data, such as DNA, RNA, chromatin accessibility, and methylation, are generated and analyzed using statistical and machine learning techniques. The course covers foundational topics such as genome organization, gene regulation, and high-throughput sequencing technologies. It extends to advanced applications including multi-omic data integration, deep learning for genomics, and cancer omics.

this course provides a broad overview of bioinformatics for single cell omics technologies, a new and fast-growing family of biological assays that enables measuring the molecular contents of individual cells with very high resolution and is key to advancing precision medicine. The course starts with an accessible introduction to basic molecular biology: the cell structure, the central dogma of molecular biology, the flow of biological information in the cell, the different types of molecules in the cell, and how we can measure them. This course then introduces students to the diverse landscape of biological data, including its types and characteristics and explores the foundational principles of single-cell omics bioinformatics, encompassing key methodologies, tools, and computational workflows, with an emphasis on the development of foundation models for single cell omics data

This interdisciplinary course is designed for students and professionals with a background in machine learning who seek to understand the fundamentals of molecular biology. The course explores key biological concepts and their applications to machine learning, particularly in the fields of bioinformatics, genomics, and computational biology. By bridging the gap between molecular biology and machine learning, students will gain the knowledge to apply machine learning techniques to biological data and solve complex problems in medicine, genetics, and drug discovery.

Computational biology has become an important discipline in the intersection of computing, mathematics, biology and medicine. Biological data sets produced by modern biotechnologies are large and hence they can only be understood by using mathematical modeling and computational techniques. Starting from analysis of genetic sequences, the field has progressed towards analysis and modeling of entire biological systems. A way of abstracting the vast amount of biomedical information is by modeling and analyzing these data by using networks (or graphs). Such approaches have been used to model phenomena in other research domains, apart from computational and systems biology and medicine.

This interdisciplinary graduate course introduces students to the core concepts, tools, and emerging methods in computational genomics and epigenomics. It is designed for students from biology, computer science, machine learning, and related fields who are interested in applying their skills to cutting-edge questions in biology and medicine. Students will explore how genomic and epigenomic data, such as DNA, RNA, chromatin accessibility, and methylation, are generated and analyzed using statistical and machine learning techniques. The course covers foundational topics such as genome organization, gene regulation, and high-throughput sequencing technologies. It extends to advanced applications including multi-omic data integration, deep learning for genomics, and cancer omics.

This course provides the students a comprehensive introduction to artificial intelligence. It builds upon fundamental concepts in machine learning. Students will learn about supervised and unsupervised learning, various learning algorithms, and the basics of the neural network, deep learning, and reinforcement learning.

Master’s thesis research exposes students to an unsolved research problem, for which they are required to propose new solutions and contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of 1 year. Master’s thesis research helps train graduates to pursue more advanced research in their PhD degree. Further, it enables graduates to pursue an industrial project involving a research component independently.