Master of Molecular Science and Software Engineering

Develop Skills in Software Engineering, Data Science, and Machine Learning for Bioinformatics and Molecular Science

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Transform your science, math, or engineering degree into a rewarding career

The Master of Molecular Science and Software Engineering (MSSE) is designed to train scientists and engineers in Software Engineering, Data Science, and Machine Learning.

With its focus on Computational Molecular Science, MSSE prepares you for Software Engineering, Data Science, Machine Learning, and Research Scientist roles in high-demand fields such as Computational Biology and Biochemistry, Bioinformatics, Biotechnology, Computational Neuroscience, Computational Chemistry, Cheminformatics, Molecular Modelling, Computational Physics, Computational Material Science, Quantum Computational Chemistry, Nanotechnology, and other Computational Science fields.

Bioinformatics Engineer

Average Starting Salary $118,474

Software Engineer

Average Starting Salary $118,997

Data Scientist

Average Starting Salary $106,916

Biotechnology Engineer

Average Starting Salary $98,973

Machine Learning Engineer

Average Starting Salary $124,742

Computational Scientist

Average Starting Salary $119,850

Source – – average national starting salary – July 2022

Skills You Will Learn

The skills that the MSSE teaches are directly transferable to other science and non-science based industries that require advanced machine learning, complex mathematical modeling and simulations, software engineering, or high-performance computing. MSSE is also an excellent preparation for students planning to pursue PhDs in the computational sciences.

Software Engineering

Computational Science

Machine Learning

Deep Learning

Data Visualization

Computational Quantum Chemistry

High Performance Computing

Leadership, Management, & Entrepreneurship

A Berkeley Quality Education

Berkeley’s Colleges of Chemistry and Engineering are consistently ranked among the top in the nation and the world.


U.S. graduate Chemistry Programs


U.S. graduate Computer Science Programs

U.S. News & World Report rankings 2024

Online Learning

MSSE courses are taught online, except for one on-campus bootcamp*. Online Learning at Berkeley means you can receive a Berkeley-quality education from anywhere in the world—no visa application required. The MSSE features small classes taught by world-renowned faculty.

*Fully online options are available.

Full-Time or Part-Time

Complete the MSSE degree in 9-months when taken full-time, or two years when taken part-time.

Learn About our Curriculum

Applications are open for a Fall 2024 program start! Apply by February 1st, 2024.

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Transform Science-Based Industries

The MSSE’s curriculum teaches computational molecular science with a focus on advanced machine learning, complex mathematical modeling and simulations, software engineering, and high-performance computing. These skills are increasingly used across a wide range of science-based industries including:

Biotech & Pharmaceutical

$3.8 Trillion

  • Drug discovery and new therapeutics
  • Vaccine development
  • Individualized medicines
  • Improved testing and screening


$1.9 Trillion

  • Renewable bioplastics
  • Reliable, durable materials for renewables
  • Biocompatible materials


$3.5 Trillion

  • Renewable energy & biofuels
  • Eco-friendly materials & chemical processes
  • Improved solar cells
  • New battery technologies

Food & Agriculture

$1 Trillion

  • Food and water security
  • Decarbonizing the synthesis of chemical feedstocks
  • New water treatment technologies


$1.9 Trillion

  • Developing advanced semi-conductor materials
  • Thermal condoctivity improvements
  • Improving yield and cost


$.7 Trillion

  • Lighter, stronger, more durable materials
  • Superalloys and exotic materials
  • Shape memory and superelastic alloys

Solving Global Challenges

Some of the world’s largest challenges can be addressed at the molecular level and computational molecular science will be a key component in this endeavor.

Climate Change

  • Eco-friendly materials and chemical processes
  • Low-carbon generation of hydrogen and other related energy carriers
  • Renewable bioplastics
  • Carbon Capture technologies
  • Decarbonising the synthesis of chemical feedstocks
  • Exascale computing to better model the world climate system

Food and Water Security

  • Increased crops yields
  • Reduced pest associated losses
  • Increased nutritional values of foods
  • New water treatment technologies to meet the growing demand for dwindling supplies

Health & Aging

  • Drug discovery
  • New therapeutics
  • Vaccines
  • Individualized medicine
  • Biocompatible materials
  • Improved testing & screening
  • Better understanding of biological processes

Renewable Energy

  • Improved solar cells
  • New battery technologies
  • Biofuels

MSSE Courses Combine



Machine Learning

Key Concepts

Computational molecular science is becoming widely used in the chemical, biochemical, and material sciences. Traditional material-development processes can take 10 to 20 years to bring a new material to market. Use of computational science in biotechnology, materials, and biomedical research can dramatically cut the time it takes to move a molecule or material from the lab to a product.

Computational Science

Harnesses computers and mathematical modeling to understand and solve complex problems in science and engineering. Its applications can range in size from the interaction of individual atoms to the behavior of weather systems and galaxies.

Molecular Science

Molecular science seeks to explore the properties and interactions of molecules at atomic, molecular, supramolecular, and system levels to develop new materials and useful interactions that solve real-world issues such as disease, world hunger, renewable energy, and environmental problems. Molecular science unifies the fields of chemistry, physics, biology, and the material sciences.

Computational Molecular Science

Combines software engineering and theoretical molecular science to model, analyze, and simulate molecular structures, properties, and interactions. The field uses specialized hardware and software, algorithm design, and large-scale data management to perform algorithmically complex, data-intensive modeling and analysis tasks.
As computational power continues to grow rapidly and as more advanced software tools and techniques become available the importance of computational molecular science to industrial and academic research will continue to grow.

High Performance Computing (HPC)

HPC enables the processing of complex calculations that use large amounts of data at high speeds. HPC typically involves using hundreds or sometimes thousands of computers or processors in parallel. This allows users to run large analytical computations, with millions of scenarios, that can use terabytes of data. HPC is used extensively in the computational molecular sciences.