Senior Research Associate

Description

Date: February 2025

Title: Senior Research Associate (Laboratory Research)

Department: Medicine – Hematology/Oncology

School: Medicine

Location: Wolstein Research Building Room 3-301

Supervisor Name and Title: Karen McColl, Lab Manager

 

POSITION OBJECTIVE

The Senior Research Associate will be responsible for a multitude of machine learning, AI,  and biomedical informatics projects for the Dowlati Lab and the Case Comprehensive Cancer Center, developing, optimizing and executing high quality computational pipelines for data management and analysis. This person will also be responsible for statistical analysis of medical record data from University Hospitals (UH) and public datasets, integrating inferences and predictions from bed to bench, and vice-versa.  They will be working on developing foundational models for patient outcome prediction. The Senior Research Associate will also deploy and use machine learning  pipelines for the integration and analysis of human clinical and multi-omics data from whole exome/genome sequencing, transcriptomic (RNA-Seq) experiments, and other sequencing projects (e.g., ATAC-Seq, ChIP-Seq, methylation assays). 

 

This position will implement and apply machine learning techniques and advanced multivariate statistical methods for responder/non-responder analysis, patient outcome predictions, and other relevant tasks. They will develop, optimize, and maintain a cloud-based infrastructure for running machine learning pipelines and analyze them on the HPC server using Python, following best practices for automation and reproducibility. It is expected that they will continuously learn and implement new methodologies to address the team's needs while also communicating statistical  analysis results effectively with academic and industry partners.

 

The Senior Research Associate will keep the PI informed of progress in the research projects and provide recommendations on machine learning/AI model preparations. They will work directly with the PI in the development of a patient outcome prediction tool and analyze the lab’s 50 terabyte plus  complex biological dataset. The candidate will also present data and findings at lab and scientific meetings, seminars, etc., help prepare manuscripts, and contribute preliminary data for grant proposals.

 

 

ESSENTIAL FUNCTIONS

1.       Develop, design, plan, execute, and interpret a multitude of machine learning analyses. This will require a strong foundation in Computer Science, Machine Learning, AI and Informatics, inter- and multi-disciplinary sciences (such as oncology, immunology, genetics, etc.), and an expert understanding of the mechanisms needed to meet the research objectives. (20%)

2.       Generate and present table summaries, plots, and models for manuscripts during lab meetings, conferences, etc. This will include the interpretation of such results, guidance for further wet-lab research, assessment of previously hypothesized projects, comparison with public datasets, etc. Such an application of bioinformatics will not only require deep understanding of mathematics and coding but also the biological significance of the results. Read publications covering a broad range of lung cancer work and bioinformatics but not limited to it. (10%)

3.       Assist the lab with editing and preparing manuscripts. (10%)

4.       Provide input, assistance, advice, edits, reviews, and prepare submission of NIH, DoD, etc. grant submissions with PI, lab members and collaborators. (10%)

5.       Perform feature selection, engineering and model development using a plethora of statistical and machine learning algorithms on both omics and clinical data (15%)

6.       Mentor/Train other informatics team members within the lab in machine learning and computer science applications in oncology (5%)

7.       Benchmark and Integrate Publicly Available Datasets: Utilize publicly available cancer datasets (e.g., TCGA, DepMap, GEO, CPTAC) to validate in-house models and hypotheses.  Develop comparative analyses between lab-generated data and external datasets to strengthen research conclusions. (10%)

8.       Explore and Apply Advanced AI Techniques: Investigate deep learning, transfer learning, and reinforcement learning approaches for complex biomedical data problems. Apply network-based or graph machine learning techniques to model gene-gene and protein-protein interactions in oncology research (10%)

 

NONESSENTIAL FUNCTIONS

1. Assist with technical issues in the lab. (2.5%)
2. Train the lab members with basic bioinformatic tools. (2.5%)
3. Present relevant research papers during journal clubs to guide lab’s research plans. (2.5%)
4. Perform other duties as assigned.  (2.5%)

 

CONTACTS

Department:

Daily contact with Dowlati lab members, lab members in adjoining labs, our collaborators, PI, administration and membership to complete position responsibilities.

 

University:

Population and Quantitative Health Sciences/Cleveland Institute of Computational Biology – for mentoring

External:

University Hospital Research coordinators for multiple clinical trials and patient sample collection updates.  University Hospitals Clinical Informatics Team for the SCLC dashboard

Students:

Contact/Interactions with students rotating through lab

 

 

SUPERVISORY RESPONSIBILITY

No direct supervisory responsibilities. Will assist/mentor lab staff and students with bioinformatic analyses inferences. Will supervise UH Clinical Informatics team for the development of the SCLC dashboard. Performance feedback will be requested as it relates to individuals serving on committees this position directs.

QUALIFICATIONS

 

Experience:  in preferred field(s) of study – lung cancer, EMR data analysis, bioinformatics, etc.  Experience developing and implementing rigorous computational methods and solutions to analyze complex biological datasets and processes. Direct experience working with large datasets is ideal, such as functional genomics (e.g. scRNA-seq data), transcriptomics, metabolomics, OR population genomics.

Education/Licensing:   Minimum of 6 years of relevant postdoctoral experience in a lab setting. Preference advanced degree in Bioinformatics, Biostatistics, Statistics, Computational Biology, Computer Science, Genome Sciences, Systems Biology, ML/AI or a similar field. PhD, MD, or other terminal degree is required.

 

 REQUIRED SKILLS

1.            Knowledge of and experience with NGS analysis such as RNA-Seq, ChIP-Seq, Exome-Seq, BS-seq analysis

2.            Proficiency in Python.  Hands-on skills using data science and bioinformatic packages

3.            Experience with using online bioinformatic tools such as XCell, MSigDb, WebGestalt, etc.

4.            Expert at navigating online cancer databases such as cBioPortal, GEO, TCGA, etc.

5.            Proficiency in High Performance Computer Unix shell scripting (eg. Vim editor) and SLURM scheduler with a background in creating pipelines for parallel bioinformatic analyses.

6.            Familiarity with cloud computing (AWS) and Spatial Sequencing (Loupe Browser).

7.            Experience in Database Management Systems (DBMS) such as PowerBI and visualization tools such as Prism or Tableau.

8.            Excellent oral and written communication skills and interpersonal skills; must demonstrate the ability to effectively and professionally communicate and work with various individuals within and external to the University.

9.            Established ability to translate biologists/project team’s scientific questions into analytical strategies and methods.

10.          Strong collaboration skills and ability to work as part of a team in an international and interdisciplinary environment.

11.          Excellent analytical skills, ability to formulate findings and recommendations from the analysis.

12.          Strong organization skills, ability to multi-task, prioritize, and meet deadlines.  Must demonstrate attention to detail and accuracy, time management skills, and follow-through. Must be able to work under pressure and conform to shifting priorities, demands, and timeline.

13.          Effective leadership skills, ability to lead, train, work with, and elicit cooperation from team members and staff. 

14.          Ability to work effectively independently and collaboratively within a team.  Must be highly motivated, responsible, dependable, and a self-starter.

15.          Effective problem-solving skills; must demonstrate innovation and creativity, sound judgment, and good decision-making.

 

 WORKING CONDITIONS

Sitting at desk for 90% of the tasks. Working indoors. Will not handle animals, biohazards or chemical hazards but may be in the same area. Travel to conferences. Walk to other buildings (CCCC and UH) for meetings with collaborators and other teams.

Qualifications

Experience:  in preferred field(s) of study – lung cancer, EMR data analysis, bioinformatics, etc.  Experience developing and implementing rigorous computational methods and solutions to analyze complex biological datasets and processes. Direct experience working with large datasets is ideal, such as functional genomics (e.g. scRNA-seq data), transcriptomics, metabolomics, OR population genomics.

Education/Licensing:   Minimum of 6 years of relevant postdoctoral experience in a lab setting. Preference advanced degree in Bioinformatics, Biostatistics, Statistics, Computational Biology, Computer Science, Genome Sciences, Systems Biology, ML/AI or a similar field. PhD, MD, or other terminal degree is required.

Equal Employment Opportunity Statement

Case Western Reserve University is an equal opportunity employer. All applicants are protected under federal and state laws and university policy from discrimination based on race, color, religion, sex, sexual orientation, gender identity or expression, national or ethnic origin, protected veteran status, disability, age and genetic information.

Reasonable Accommodation Statement:
Case Western Reserve University complies with the Americans with Disabilities Act regarding reasonable accommodations for applicants with disabilities. Applicants requiring a reasonable accommodation for any part of the application and hiring process should contact the Office of Equity by phone at 216.368.3066 or by email at equity@case.edu. Determinations as to granting reasonable accommodations for any applicant will be made on a case-by-case basis.

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