IT Consultant - Machine Learning & Artificial Intelligence

All Job Postings will close at 12:01a.m. CST (1:01a.m. EST) on the specified Closing Date (if designated).

If you close the browser or exit your application prior to submitting, the application progress will be saved as a draft. You will be able to access and complete the application through “My Draft Applications” located on your Candidate Home page.

Job Posting Title:

IT Consultant - Machine Learning & Artificial Intelligence

   

Position Type:

Professional / Unclassified

   

Department:

LSUAM ORED - CCT - Scientific Computing (Michal Brylinski (00004691))

   

Work Location:

2054 Digital Media Center

   

Pay Grade:

Professional

   

Job Description:

The Center for Computation and Technology at Louisiana State University is seeking a highly motivated and talented researcher specializing in Machine Learning (ML) and Artificial Intelligence (AI) to join our multidisciplinary research team. This role offers a unique opportunity to contribute to cutting-edge biomedical research while also developing innovative ML and AI methodologies. The successful candidate will collaborate with leading researchers in biomedicine, applying ML and AI technologies to fields such as drug discovery, cancer research, genomics, and proteomics. In addition, the candidate will be encouraged to pursue independent research focused on advancing ML and AI technologies, particularly those that address challenges within the biomedical domain.

Job Responsibilities:

Collaborative Research: Work closely with biomedical research teams to design, implement, and optimize ML/AI models tailored to their specific needs. Contribute to projects in areas such as, but not limited to drug discovery and design using predictive models, cancer research with a focus on precision medicine, genomics and proteomics data analysis for biomarker discovery and pathway analysis, integration of multi-omics data to gain insights into complex biological systems. Provide ML/AI expertise in experimental design, data analysis, and interpretation of results. (45%)

Independent Research: Develop novel ML/AI algorithms, frameworks, or architectures that address emerging challenges in biomedicine. Explore and publish on topics such as, but not limited to explainability and interpretability of ML models in biomedical research, transfer learning and domain adaptation for scarce biomedical datasets, scalable algorithms for processing high-dimensional omics data, development of generative models for simulating biological processes. (30%)

Outreach and Training: Collaborate with interdisciplinary teams, bridging the gap between computer science and life sciences. Mentor students, interns, or junior researchers in ML/AI methodologies. Contribute to workshops, seminars, and collaborative grant-writing efforts. (10%)

Publication and Dissemination: Publish high-impact papers in top-tier ML/AI and biomedical journals and conferences. Present findings at academic and professional conferences, fostering visibility for the research center. (10%)

Other duties as assigned (5%)

Minimum Qualifications:

Master's degree in Computer Science, Data Science, Computational Biology, or a closely related field with a strong emphasis on ML/AI and 2+ years of experience.

Have proven experience in applying ML/AI to biomedical or life science data, such as genomics, proteomics, or clinical datasets. Experience in ML frameworks and tools (e.g., TensorFlow, PyTorch, Scikit-learn). Strong programming skills in Python, R, or equivalent languages.

Excellent problem-solving skills, with the ability to analyze and interpret complex data. A track record of peer-reviewed publications in relevant journals or conferences. Strong communication and collaboration skills for interdisciplinary teamwork.

Preferred Qualifications:

Ph.D. in Computer Science, Data Science, Computational Biology, or a closely related field with a strong emphasis on ML/AI and 2+ years of experience.

Experience in one or more of the following: deep learning architectures (e.g., transformers, graph neural networks), reinforcement learning or unsupervised learning in biological contexts, omics data integration or high-throughput screening data analysis, natural language processing applied to biomedical literature or electronic health records, demonstrated ability to secure research funding or contribute to grant proposals, familiarity with high-performance computing or cloud-based ML/AI workflows.

   

Additional Job Description:

Special Instructions:

Please provide a cover letter, resume, and three (3) professional references including name, title, phone number,