Biomedical Informatics

Biomedical Informatics Subject Matter Experts (SMEs

Job Description

Role Overview :

We are seeking highly skilled Biomedical Informatics Subject Matter Experts (SMEs) to join our team in developing rigorous, high-quality evaluation questions designed to assess and challenge advanced AI models. The primary objective is to create benchmark questions that test the boundaries of AI capabilities across diverse bioinformatics domains.

This role requires deep technical expertise combined with the ability to craft precise, unambiguous questions with well-defined evaluations. The ideal candidate will possess not only strong theoretical knowledge but also practical experience in designing assessment materials that have deterministic, verifiable outcomes.

Responsibilities:

- Design and develop challenging bioinformatics coding questions that push the limits of AI model capabilities.
- Create automated, coding-based evaluations with unit tests that objectively verify correctness.
- Build comprehensive evaluation rubrics with 7–10 distinct criteria for each bioinformatics question.
- Ensure every question is self-contained, well-defined, and completely unambiguous, including all domain rules and edge cases.
- Develop problems where there is exactly one correct answer or outcome that can be deterministically validated.
- Translate theoretical bioinformatics concepts into concrete, measurable outputs.
- Enforce performance requirements with explicit constraints and tests to prevent inefficient or brute-force solutions.

Required Qualifications:

Educational Background

- Master's degree of experience in biomedical data processing and interpretation.
- Ongoing PhD with 3+ years of experience in biomedical data processing and interpretation.
- PhD (completed) in Biomedical Informatics, Bioinformatics, Computational Biology or a closely related field (preferred).

Technical and other skills

- Strong proficiency in biomedical data processing, analysis, and interpretation, including working with large‑scale datasets.

- Experience with bioinformatics tools and pipelines (e.g., NGS workflows, multi‑omics, etc.).
- Solid programming skills in Python / R
- Experience working in Linux/Unix, Docker and version control.
- Strong communication and collaboration skills for working in interdisciplinary research teams

Domain Expertise Requirements

Candidates must demonstrate deep, expert-level knowledge in at least TWO (2) of the following five core bioinformatics domains:

1. Biomedical Image Processing

- Processing of Radiology imaging: CT, MRI, PET/SPECT, Ultrasound imaging
- Automating cell counting, identifying cell types, and analyzing tissue structures in pathology slides
- Computational segmentation and boundary inference
- Medical image formation, reconstruction & enhancement
- Noise modeling and statistical denoising
- Image sampling theory and resolution limits
- Intensity standardization and harmonization
- Geometric transformations and spatial calibration
- Multi-modal image registration and alignment
- Morphological and topological shape analysis
- Quantitative imaging feature extraction
- Fusing data from different modalities (MRI, PET, Genomics) for a comprehensive view to tailor treatments
- pattern recognition and predictive modeling

2. Biomedical Signal Processing

- Cardiovascular/hemodynamic signals (ECG, PPG, BP, HRV, etc)
- Neural/brain electrical signals (EEG, EP/ERP; seizure/cognitive analysis)
- Neuromuscular/motor signals (EMG, motor unit decomposition; prosthetics/rehab)
- Ocular/visual system signals (EOG, blink/saccade analysis)
- Respiratory signals (airflow, effort; sleep/disordered breathing)
- Nonlinear, Statistical, and Adaptive Signal Analysis
- Multichannel, Spatial, and Connectivity Signal Analysis
- Machine Learning and Data‑Driven Biomedical Signal Analysis
- Clinical Monitoring, Wearable, and Translational Signal Processing

3. Molecular & Omics based Personalized Medicine

- Omics based risk / disease prediction (GWAS, PRS, bulk, single‑cell RNA‑seq, proteins / metabolite signatures associated with diseases)
- Risk Prediction and Prognosis Modeling
- Molecular patient subtyping
- Liquid Biopsy and Circulating Biomarkers (ctDNA,…