Assistant Moffitt Research Jobs (NOW HIRING)

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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 departmentComprehensive 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). Contribute to current and initiate future machine learning applications at Moffitt, as evidenced by a history of peer-reviewed publications and involvement in grant-supported research projects. Ongoing research includes but is not limited to outcome modeling, human-machine interaction, clinical decision support, information retrieval, quantitative imaging (radiomics), digital pathology (pathomics), and computational biology, among others, where machine learning can accelerate cancer discovery and improve care delivery. Credentials and Qualifications: Doctorate degree in computer science, engineering, Physics, mathematics, statistics or a relevant field with appropriate research training and experience. Academic rank beyond Assistant Member will be commensurate with experience and qualifications. Moffitt is affiliated with the University of South Florida, and a University appointment is available in the rank of Assistant/Associate Professor as applicable in the appropriate departments. Moffitt-based faculty members focus their efforts on research, with minimal expectations for formal teaching. Tampa is a thriving metropolitan city that provides its residents with a high quality of life. The Tampa Bay area has become a hub for groundbreaking research, welcoming individuals from around the globe. This diverse city is engulfed with rich culture, year-round activities for all, beautiful beaches, amazing cuisine, and so much more. Questions regarding the position should be directed to: Issam El Naqa, PhD, FIEEE, Search Committee Chair, Department of Machine Learning (Issam.Elnaqa@moffitt.org).

Salary Range

Salary ranges posted for this position represent the expected base pay range for the role. Actual compensation may vary based on location and a variety of job-related factors, including experience, skills, education, and internal equity among Team Members in similar positions.

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