Postdoctoral Research Fellow

With supervision and guidance (under PI mentorship, but with increasing independence), responsibilities of the Postdoctoral Research Fellow will include:

• Algorithm R&D (primary focus): Invent and prototype machine learning models for medical time series and multimodal data (e.g., EEG/EHR/video/wearables): self-/semi-supervised learning, representation learning, sequence models (TCN/Transformer), multimodal fusion, uncertainty & calibration, domain adaptation, and active learning.

• Signal processing for neuro/physio data: Build robust preprocessing and feature pipelines (artifact removal/ICA, filtering, time-frequency transforms, connectivity/graph features) and benchmarking baselines.

• Model evaluation at clinical standards: Design rigorous evaluation plans (nested CV; AUROC/AUPRC; sensitivity/specificity at fixed thresholds; calibration/decision-curve analysis; subgroup/fairness slices; robustness, drift, and ablation studies).

• Data engineering for research: Own data curation/QC, labeling protocols, and dataset versioning; create task definitions/labels that reflect clinical intent and minimize information leakage.

• Reproducible ML: Maintain clean, well-tested code; containers and experiment tracking; model/data cards; automated training/validation scripts.

• Results synthesis: Turn findings into clear figures and narratives; present at lab meetings and conferences; lead/assist manuscripts to top venues.

• Collaboration & mentoring: Partner with clinicians/biostatisticians on problem framing and endpoint selection; mentor students/RAs on analysis best practices.

• Grants & broader studies: Contribute preliminary analyses and methods text to grant applications; support related AI studies across the lab.

• Work with coordinators/clinicians on cohort definitions, labeling, and feasibility.

• Follow basic IRB/HIPAA data-handling norms within established lab workflows (no heavy RA/QA ownership).

• SKILLS/ABILITIES/COMPETENCIES REQUIRED: Must be realistic, objective, measurable and related to essential functions of this job.

  A successful candidate will:

• Work independently under the general guidance of the Principal Investigator.
• Be self-motivated, meticulous, highly organized, and detail-oriented.
• Demonstrate initiative and a rapid ability to learn new methods and tools.
• Exhibit strong analytical thinking and problem-solving skills.
• Possess advanced technical aptitude and data science proficiency.
• Communicate exceptionally well in writing and orally, as evidenced by publications and presentations.
• Make sound, timely, and independent decisions using good scientific judgment.
• Be proficient with contemporary research techniques and methodologies.
• Display excellent interpersonal skills and professional conduct.
• Thrive as a collaborative team member who works effectively across the lab.

• EDUCATION: Specify minimum education and clearly indicate if preferred or required

• Possess or be on track to complete a PhD or MD with background in computer science, physics, computational science, machine learning, artificial intelligence, deep learning, computer vision, large language model, image processing, biomedical engineering, bioinformatics, visual science, biochemical engineering or a related quantitative science field.

• EXPERIENCE: Specify minimum creditable years of experience and clearly indicate if preferred or required

• Research experience and/or publications in computer vision, large language models (LLMs), vision science, and biomedical signal processing-especially EEG waveform analysis. (++)
• Experience with facial expression analysis, affective computing, and sentiment analysis. (+++)
• Strong interest and background in biomedicine, chemical engineering, neuroscience and clinical research, with hands-on neurophysiology analytics (EEG/EMG) experience: end-to-end processing pipelines, artifact handling, time-frequency features, source modeling, and/or deep sequence models. (++)
• Experience with multimodal learning (e.g., EEG + video/voice/wearables); self-supervised or foundation models; interpretability (causal/counterfactual analyses); and on-device or real-time inference. (+)
• Excellent scientific writing and presentation skills. (++)
• Software product development experience in Python. (+)

• SUPERVISORY RESPONSIBILITY

  Assistance guiding research technicians and trainees in the lab is expected.

  WORKING CONDITIONS:

  Work will be performed in a fast-paced translational research and clinical environment, requiring teamwork, collaboration, and flexibility. Must be able to work independently.

• Other duties as assigned in support of the research program.