Postdoctoral Fellow - AI for Drug Discovery (Bissan Al-Lazikani Lab)
- Requisition #: 600138-202504181059
- Department: Genomic Medicine
- Location: Houston, TX
- Posted Date: 4/17/2025
A postdoctoral fellow position is available in the Department of Genomic Medicine. We seek a driven and technically adept Postdoctoral Fellow to develop and deploy Artificial Intelligence models to advance cancer drug discovery by solving key challenges, from target identification to drug design and development.
LEARNING OBJECTIVES
Gain an understanding of key components and bottlenecks in cancer drug discovery. Develop an understanding of a hands-on experience in data science analytics and model development to address these bottlenecks. This includes learning to train, benchmark, and deploy deep neural networks and foundation models. Learn to apply iterative, human-in-the-loop AI models in the context of cancer drug discovery and validate these models in collaboration with experimental researchers. Develop hands-on expertise in AI applications within one or more key areas: patient profiling and imaging data science; structural biology and protein structure prediction; drug design; and forecasting models for drug resistance.
ELIGIBILITY REQUIREMENTS
Individuals with a PhD in computer science and a strong desire to apply and advance their skill set with chemical and biological structural data are encouraged to apply. A strong computational background is required. Expertise with PyTorch and torch-geometric, or equivalent technologies, is essential. Experience with other deep learning and generative methods is preferred. Experience working in a high-performance computing environment (Unix/Linux) using state-of-the-art GPU computing is required. Alternatively, candidates with a PhD in computational biology and demonstrable experience in Python, Jupyter Notebook, or equivalent technologies, applying bioinformatics or computational techniques to biomedical data, are encouraged to apply.
Some understanding of databases related to several of the following-cancer genomes, structural biology, and chemistry is highly desired. Importantly, candidates' eagerness to grow in these related fields is crucial. This position will involve collaboration with multidisciplinary partners, requiring excellent written and verbal communication skills. The role will also include reporting and sharing findings with the broader cancer and drug discovery research community through published articles and presentations.
ADDITIONAL APPLICATION INFORMATION
The appointment is immediately available. The appointee is expected to work forty hours per week, including instruction and literature studies in the computational life sciences to deepen the successful candidate's knowledge beyond data science alone.
This position reports to Dr. Bissan Al-Lazikani in the Genomic Medicine Department.
Consideration of applications will begin immediately and continue until the position is filled.
POSITION INFORMATION
MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition08/31/2025
LEARNING OBJECTIVES
Gain an understanding of key components and bottlenecks in cancer drug discovery. Develop an understanding of a hands-on experience in data science analytics and model development to address these bottlenecks. This includes learning to train, benchmark, and deploy deep neural networks and foundation models. Learn to apply iterative, human-in-the-loop AI models in the context of cancer drug discovery and validate these models in collaboration with experimental researchers. Develop hands-on expertise in AI applications within one or more key areas: patient profiling and imaging data science; structural biology and protein structure prediction; drug design; and forecasting models for drug resistance.
ELIGIBILITY REQUIREMENTS
Individuals with a PhD in computer science and a strong desire to apply and advance their skill set with chemical and biological structural data are encouraged to apply. A strong computational background is required. Expertise with PyTorch and torch-geometric, or equivalent technologies, is essential. Experience with other deep learning and generative methods is preferred. Experience working in a high-performance computing environment (Unix/Linux) using state-of-the-art GPU computing is required. Alternatively, candidates with a PhD in computational biology and demonstrable experience in Python, Jupyter Notebook, or equivalent technologies, applying bioinformatics or computational techniques to biomedical data, are encouraged to apply.
Some understanding of databases related to several of the following-cancer genomes, structural biology, and chemistry is highly desired. Importantly, candidates' eagerness to grow in these related fields is crucial. This position will involve collaboration with multidisciplinary partners, requiring excellent written and verbal communication skills. The role will also include reporting and sharing findings with the broader cancer and drug discovery research community through published articles and presentations.
ADDITIONAL APPLICATION INFORMATION
The appointment is immediately available. The appointee is expected to work forty hours per week, including instruction and literature studies in the computational life sciences to deepen the successful candidate's knowledge beyond data science alone.
This position reports to Dr. Bissan Al-Lazikani in the Genomic Medicine Department.
Consideration of applications will begin immediately and continue until the position is filled.
POSITION INFORMATION
MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition
