Postdoctoral Research Fellow - McWilliams School of Biomedical Informatics

The Kim Lab at the University of Houston is seeking a Postdoctoral Fellow in computational antibody engineering and AI-driven drug design. You will work directly with Dr. Yejin Kim at the intersection of machine learning, structural biology, and therapeutic antibody discovery — with close, day-to-day collaboration with a dedicated wet-lab validation team embedded within the same group.

We are part of the Department of Health Data Science and Artificial Intelligence at UTHealth, home to a collaborative research environment with access to H100/H200 GPU-accelerated compute servers, a cryo-EM facility, and a therapeutic antibody core facility. Recent project scope includes, but is not limited to, language model-based antibody affinity maturation, de novo antibody design, and antibody discovery using B cell repertoire. Houston is home to the Texas Medical Center, the largest medical center in the world, giving our team direct access to clinical collaborators, translational resources, and a thriving biotech ecosystem.

Candidates must be self-driven, with a demonstrated record of completed projects and publications showing the ability to move fluidly between computational modeling and experimental interpretation.

Key areas of Contribution:

Research & Scholarship

• Conceptualize original research questions at the intersection of machine learning, structural biology, and therapeutic antibody engineering
• Lead the design and execution of computational research programs, from hypothesis generation through manuscript preparation
• Author and co-author papers targeting high-impact journals in computational biology, structural biology, and chemical biology (e.g., Nature, PNAS, Nature Communications, npj digitial medicine, NeuralIPS)
• Present findings at national and international conferences, building your independent scientific profile

Computational Development

• Design, implement, and benchmark ML pipelines for mutation effect prediction, fitness landscape modeling, and generative antibody design using protein language models (ESM2/ESM3) and structure-based scoring
• Build reproducible, version-controlled workflows for AlphaFold2/3, Rosetta/FoldX ddG estimation, and antibody–antigen complex analysis
• Develop and maintain shared computational infrastructure and documentation for team-wide use
• Wet-Lab Collaboration
• Work closely with wet-lab partners to translate computational predictions into experimentally testable hypotheses — design the experiment, not just the model
• Interpret SPR, BLI, ELISA, and SEC-HPLC data and feed results back into model retraining and active learning loops
• Participate in joint design–test–learn meetings, co-authoring experimental design documents spanning both teams
• Triage antibody candidates for developability liabilities — deamidation, oxidation, aggregation hotspots — in collaboration with the characterization team.

Team & Mentorship

• Apply structured problem-solving approaches to keep project workstreams on track and deliverables at high quality
• Mentor graduate students and junior lab members in computational methods, antibody biology, and scientific communication
• Contribute to grant writing and support proposal development for new research directions

What we do here changes the world. UTHealth Houston is Texas’ resource for healthcare education, innovation, scientific discovery, and excellence in patient care. That’s where you come in.

Once you join us you won't want to leave. It’s because we reward our team for the excellent service they provide. Our total rewards package includes the benefits you’d expect from a top healthcare organization (benefits, insurance, etc.), plus:

• 100% paid medical premiums for our full-time employees
• Generous time off (holidays, preventative leave day, both vacation and sick time – all of which equates to around 37-38 days per year) 
• The longer you stay, the more vacation you’ll accrue! 
• Build your future with our awesome retirement/pension plan! 

We take care of our employees! As a world-renowned institution, our employees’ wellbeing is important to us. We offer work/life services such as... 

• Free financial and legal counseling 
• Free mental health counseling services 
• Gym membership discounts and access to wellness programs 
• Other employee discounts including entertainment, car rentals, cell phones, etc. 
• Resources for child and elder care 
• Plus many more! 

Position Summary:

A biomedical postdoc position provides an opportunity for early-career researchers to gain valuable experience, develop independent research skills, and contribute to the advancement of scientific knowledge in their field of expertise.

Position Key Accountabilities:

1. Plans and conducts experiments, analyzes data, and prepares publications describing results.
2. Assists in training and mentoring of lab personnel, including graduate students and other trainees.
3. Reads and evaluates literature.
4. Possess interpersonal skills to effectively collaborate and communicate with individuals at all levels.
5. Have strong written and oral communication skills. 
6. Performs other duties as assigned.

Preferred Qualifications:

• PhD in computational biology, bioinformatics, immunology, structural biology, biochemistry, or a closely related field
• Working knowledge of antibody biology: CDR structure, germline gene usage, VH/VL pairing, somatic hypermutation, affinity maturation mechanisms, and antibody–antigen recognition
• Familiarity with B cell biology and the humoral immune response, including germinal center reactions and clonal selection[TYC1] 
• Strong Python programming skills with hands-on experience building and evaluating machine learning models (PyTorch or JAX); ability to write and maintain research-grade software
• Experience with protein structure prediction or molecular modeling tools (AlphaFold2/3, Rosetta, FoldX, OpenMM, or equivalent)
• Comfort working in a Linux/HPC environment with version control (Git) and reproducible workflow practices
• Strong written and oral communication skills — ability to present computational findings clearly to mixed computational and experimental audiences
• Demonstrated ability to work independently and drive projects from conception to publication
• Collaborative mindset: comfort working at the interface of computational and wet-lab teams, translating model outputs into experimental hypotheses and integrating assay results back into the modeling cycle
• Experience with machine learning and antibody engineering
•  Hands-on experience with antibody-specific language models — AbLang, AntiBERTy, ESM2/ESM3, IgLM, or equivalent — for sequence design, mutation scoring, or affinity prediction
• Familiarity with zero-shot or fine-tuned PLM strategies for predicting the effect of mutations on binding affinity (ΔΔG estimation, fitness landscape modeling)
• Experience with deep mutational scanning datasets or related benchmarks (e.g., AbBiBench, ProteinGym) for model evaluation
• Experience with generative protein design tools — RFdiffusion (including the antibody-fine-tuned variant), ProteinMPNN, RFantibody, or AntiFold — for CDR loop design and sequence optimization
• Familiarity with structure-based filtering and validation pipelines: RoseTTAFold2, ABodyBuilder3, or equivalent antibody structure predictors
• Experience designing or evaluating antibody–antigen interfaces computationally, including epitope targeting and paratope modeling
• Experience with AIRR-seq (BCR repertoire) data analysis: V(D)J annotation, clonal lineage tracing, somatic hypermutation profiling, and CDR3 clustering
• Familiarity with repertoire analysis tools and frameworks: IMGT/ANARCI, Change-O, Immcantation, or airflow
• Experience mining OAS, SAbDab, or iReceptor for training data construction and antigen-specific clone discovery
• Knowledge of antibody developability frameworks: deamidation, oxidation, aggregation hotspot prediction, and humanization strategies
• Prior experience with yeast display, phage display, or single B cell sorting in a collaborative wet-lab setting
• Active learning or Bayesian optimization experience for closed-loop experimental design

Minimum Education:

Doctoral/Terminal Degree

Physical Requirements:

Exerts up to 20 pounds of force occasionally and/or up to 10 pounds frequently and/or a negligible amount constantly to move objects.

Security Sensitive:

This job class may contain positions that are security sensitive and thereby subject to the provisions of Texas Education Code § 51.215

Residency Requirement:

Employees must permanently reside and work in the State of Texas.

This position is a security-sensitive position pursuant to Texas Education Code §51.215 and Texas Government Code §411.094. To the extent that a position requires the holder to research, work on, or have access to critical infrastructure as defined in Texas Business and Commerce Code §117.001(2), the ability to maintain the security or integrity of the infrastructure is a minimum qualification to be hired for and to continue to be employed in that position. Personnel in such positions, and similarly situated state contractors, will be routinely reviewed to determine whether things such as criminal history or continuous connections to the government or political apparatus of a foreign adversary might prevent the applicant, employee, or contractor from being able to maintain the security or integrity of the infrastructure. A foreign adversary is a nation listed in 15 C.F.R. §791.4.