ASSISTANT PROFESSOR - RESEARCH

LSU; New Orleans (LA); On-site; USD 90,000 - 120,000; Full time

Job summary

Louisiana State University Health Sciences Center (LSUHSC) - BCM Training Program invites applications for an Assistant Professor - Research in bioinformatics and computational medicine. The candidate will train graduate and medical students and lead innovative research blending ML/AI with multi-omics data.

The role emphasizes collaboration with a multidisciplinary team and the development of an independent funding trajectory over time.

Qualifications

• PhD required in Bioinformatics, Computational Genomics, Computer Science, computer engineering or closely related field.
• 1 year of experience required to perform the job; publication record in bioinformatics, computational genomics, ML and AI.

Responsibilities

• Teach courses and mentor MS Bioinformatics Track and MD/MS Computational Medicine Track students.
• Serve as Assistant Director to the BCM Training Program, providing strategic and technology solutions and maintaining infrastructure.
• Conduct biomedical research focused on ML/AI methods applied to multi-omics data and gene regulatory networks.
• Develop an independent research program and secure funding over the long term.

Skills

Machine Learning

Artificial Intelligence

Bioinformatics

Computational Genomics

Education

PhD

Job description

Job Summary/Basic Functions Louisiana State University Health Sciences Center (LSUHSC) -School of Medicine’s Bioinformatics and Computational Medicine (BCM) Training Program in the Department of Genetics invites applications for a junior faculty position at the rank of Assistant Professor - Research, in the field of bioinformatics and computation medicine. This is an exciting opportunity for a talented candidate to contribute to training the next generation of scientists and physician scientists and conducting groundbreaking research in the fields of bioinformatics, computational medicine, Machine Learning and Artificial Intelligence with application to biomedical research. The successful candidate will work in a highly collaborative and supportive environment, working closely with a multidisciplinary team of bioinformaticians, basic and translational researchers to advance the research, education and service missions of the School of Medicine.

The successful candidate will:

1. (1) Be involved in teaching courses, training and mentoring graduate students enrolled in the MS Bioinformatics Track and medical students enrolled in the MD/MS Computational Medicine Track. The successful candidate will serve as Assistant Director to the Director of the BCM Training Program, providing strategic and technology solutions, developing education and training as well as maintaining the software and technology infrastructure critical to the development and advancement of the training program.
2. (2) Be involved in conducting biomedical research focused on development and application of advanced bioinformatics and computation genomics methods and algorithms specifically, Machine Learning and AI with application to analysis and integration of large-scale multi-omics data, modelling complex gene regulatory networks and predicting disease threats.

In the long term, it is expected that the successful candidate will develop an independent line of research and source funding.

Minimum Qualifications

• Required Education: PhD
• Required Field of Study: Bioinformatics, Computational Genomics, Computer Science, computer engineering or closely related field
• Number of years required to perform the job: 1
• Type of experience required to perform the job: Demonstrated experience supported by a publication record in bioinformatics, computational genomics, Machine Learning and AI.

Preferred Qualifications

• Preferred Education: PhD
• Preferred Discipline: Bioinformatics, Computational Genomics, Computer Science, computer engineering or closely related field.
• Preferred Experience: PhD 1-2 years postdoc with experience supported by a strong publication record in bioinformatics and computational genomics.
• Demonstrated experience in multiplatform multiscale multi-omics data analysis and integration.