Machine Learning Scientists/Engineers, Cambridge, UKAstex Pharmaceuticals is a global leader in innovative drug discovery, utilizing its proprietary Fragment-Based Drug Discovery platform to develop new drug candidates, with several progressing in clinical trials. The company focuses on Neurological Disorders and Oncology.

The Role

We are seeking Machine Learning Scientists/Engineers for permanent and 3-year fixed-term contracts to develop innovative machine learning methods for structure-based drug discovery. You will work on advancing computational tools that impact the design and optimization of therapeutics, leveraging extensive proprietary datasets and cutting-edge AI techniques in a collaborative environment.

Responsibilities:

Design and implement ML models for structure-based drug design, including protein-ligand interaction modeling and co-folding applications.

Develop AI approaches integrating structural and chemical data to enhance virtual screening and molecular design.

Utilize proprietary structural datasets to train, benchmark, and validate algorithms.

Collaborate with multidisciplinary teams to translate research into production solutions.

Profile and Skills:

PhD or equivalent in a technical discipline (e.g., computer science, chemistry, physics, engineering).

Strong background in machine learning, including deep learning and generative models.

Experience with ML frameworks such as PyTorch, TensorFlow, or JAX.

Proficiency in Python and C++, with a collaborative mindset.

Familiarity with protein structure modeling, co-folding, or related methods; knowledge of organic chemistry is a plus.

Why Join Astex

We offer excellent training, career development, competitive salary, and benefits, including hybrid working options. Located near Cambridge City Centre, with onsite facilities and good transport links, we embrace diversity and are committed to an inclusive workplace.

More Information

Visit

www.astx.com

for Astex Pharmaceuticals and

www.otsuka.co.jp

for Otsuka Pharmaceuticals.

Application Process

Apply online at

this link

before April 30, 2025. Please note email addresses are modified; change ",+" back to "@" before emailing.

Learn about the job from the Computational Chemistry List Job Listing at

https://server.ccl.net/jobs .

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