Assistant Member Tenure-earning in the Department of Machine Learning at Moffitt Cancer Center

Working at Moffitt is both a career and a mission: to contribute to the prevention and cure of cancer.

As the only National Cancer Institute-designated Comprehensive Cancer Center based in Florida, Moffitt employs some of the best and brightest minds from around the world. Join a dedicated team of nearly 11,000 who are shaping the future we envision. Moffitt has been recognized as a Best and Brightest Company to Work For in the Nation and is continually named one of the Tampa Bay Times’ Top Workplaces.

 

Summary

The faculty member will develop and maintain an active research program. The faculty member will support his/her research primarily through extramural grants and publish original research reports in peer-reviewed scientific journals. The faculty member will recruit and appropriately mentor research personnel within his/her research program. The faculty member will actively and collegially participate in the CCSG programs and the Moffitt Research Institute activities.The Independent Scientist Pathway is intended for tenure-track individuals who dedicate most of their effort to independent research. A portion of their effort is dedicated to educating future investigators, by teaching graduate students and medical students, and supervising postdoctoral fellows. These individuals will have their primary appointment in a Basic Science or Population Science department. They may have a secondary appointment in a Clinical Science department

 

Comprehensive Benefit Package | Relocation Assistance | Start-Up Package & Research Incentive Plan   The Department of Machine Learning (ML) at Moffitt Cancer Center, a National Cancer Institute-designated Comprehensive Cancer Center, is seeking a new faculty member in the tenure-earning rank of Assistant Member with research interests in artificial intelligence, decision support systems, machine and deep learning, federated learning, and their application in cancer discovery and clinical care. The new faculty will join an expanding ML Department. We currently have faculty initiating a wide range of machine learning research and its application in oncology in collaboration with other members of the Quantitative Science Division, including the Biostatistics and Bioinformatics Department and the Integrated Mathematical Oncology Department, as well as with other research and clinical departments within the cancer center. Moffitt Cancer Center is characterized by a culture of collegiality and team science, facilitating cross-disciplinary collaborations for cancer research and mentoring.  Faculty development is a tenet of Moffitt culture and an essential part of this department’s philosophy to develop future leaders in the emerging field of machine learning in oncology. Position Highlights: • Access to extensive retrospective and prospective data for real-world predictive analytics, and other clinical research resources, including an integrated repository of clinical, genomic, imaging, and patient-reported information as well as biospecimens from a large cohort of patients.  • Collaboration with implementation scientists offers the opportunity to integrate machine learning algorithms into the electronic medical record and clinic workflows to improve clinical care.  • Access to Moffitt’s extensive computational and rich data resources such as the ML Department features state-of-the-art DGX-A100/DGX-H100/DGX-H200 cluster and machine learning engineers for advanced machine learning applications with retrospective and prospective comprehensive clinical datasets, with a focus on data integration and personalized cancer care. The Ideal Candidate: • Expertise in artificial intelligence, decision support systems, machine and, deep learning, federated learning, who are interested in applying this expertise to cancer research and translational oncology.  • Preference will be given to applicants with an outstanding record conducting team science or collaborative research with an emphasis on machine learning in healthcare. Areas of interest include: the applications of deep learning, federated learning, explainability and interpretability of machine learning in outcome modeling, human-machine interaction, clinical decision support, and information retrieval. • Demonstrate experience (or potential) as a collaborative or independent researcher with extramurally funded research studies, presentations at national and international conferences, and a record of high-quality peer-reviewed publications. Responsibilities: • Maintain a productive integrated and/or independent research program in machine learning in oncology. • Collaborate on a variety of machine learning research projects both within Moffitt and externally. • Engage in educational (e.g., mentorship) and service activities across Moffitt and its affiliates (AI in cancer with USF). • C