Duties: Configures, debugs and ensures stable operation of HPC cluster tools such as Bright Cluster Manager, OpenHPC, Warewulf; Configures, optimizes and ensures stable operation of software such as MATLAB and open source equivalents such as Octave and SciLab. In addition, ensures that any added module or toolbox needed for this software is working properly, including necessary licenses; Configures, optimizes and ensures stable operation and availability of Message Passing Interface (MPI) such as OpenMP libraries and utilities; Configures, optimizes and ensures stable operation and availability of the R statistical package and necessary extra modules needed by the HPC cluster users; Configures, optimizes and ensures stable operation of GPU related software and libraries such as CUDA and others; Configures, optimizes and ensures stable operation of specific software needed for mathematical oncology, bioinformatics, biostatistics and any other groups of research involved in the use of the HPC cl...
Requirements: Bachelor’s Degree in Computer Science, Information Technology, Management Information Systems, Biology/Chemistry/Physics or related field; Minimum of seven (7) years' experience designing, developing and successfully administering or supporting Unix- based systems

Duties: Develop and extend machine learning methods for big cancer Omics data such as single-cell omics data and spatial transcriptomics data; Develop bioinformatics pipelines for analyzing high dimensional immunological data; Develop novel visualization tools for interpreting big cancer Omics data; Discover new predictive biomarkers for cancer prognosis; Collaborate on a variety of cancer and bioinformatics research projects
Requirements: Research background/experiences in Bioinformatics, Statistics, Computational Biology, Machine Learning, or Computer Science; A highly motivated and independent researcher with a strong quantitative scientific background; Programming experience in statistical program R and/or scripting languages such as Perl/Python on Unix/Linux systems; Experience in cancer genomics is not required but the ability to learn new knowledge is highly desirable; Excellent communication and writing skills; Experience of integration of multi-omics data such as WES, RNA-seq, metabolomics, proteomics, etc, is preferred

Duties: Work on unique, multi-omics datasets to support translational lung cancer metabolism research; Develop cutting-edge methods for processing, analyzing, and interpreting multi-omics data; Enjoy a close working relationship with bioinformaticians, statisticians, bench scientists, and translational researchers to establish a well-rounded multidisciplinary team; Join an institute that highly values computational research
Requirements: PhD in Bioinformatics, Data Science, Computational Biology, Machine Learning, Statistics/Biostatistics, or related fields; Experience working with at least one “omics” type (e.g., DNA-seq, RNA-seq, metabolomics, proteomics); Preference will be given to candidates with experience in integration of multi-omics data; Programming experience in statistical program R and/or scripting languages such as Python/Perl on Unix/Linux systems; Highly motivated and independent researcher with a background in cancer biology; Excellent communication and writing skills

Duties: Develop and extend machine learning methods for big cancer Omics data such as single-cell omics data and spatial transcriptomics data; Develop bioinformatics pipelines for analyzing high dimensional immunological data; Develop novel visualization tools for interpreting big cancer Omics data; Discover new predictive biomarkers for cancer prognosis; Collaborate on a variety of cancer and bioinformatics research projects
Requirements: PhD in Bioinformatics, Statistics/Biostatistics, Applied Mathematics, Data Science, Computer Science, or related fields. Research background/experiences in Bioinformatics, Statistics, Computational Biology, Machine Learning, or Computer Science. A highly motivated and independent researcher with a strong quantitative scientific background. Programming experience in statistical program R and/or scripting languages such as Perl/Python on Unix/Linux systems

Duties: Under the supervision of the PI, conducts advanced analysis requiring programming in Stata, SAS, R, Python, or other relevant statistical software package. Analysis may include but is not limited to survival analysis, case‐control analyses, cohort analyses, etc; Performs bioinformatic data quality control prior to analysis implementation; Determines appropriate statistical methods to achieve project aims; Collects and manages survey, insurance claims, and clinical data. Develops custom databases with oversight from the PI; Collects big data from multiple resources and determining appropriate merging procedures with existing analytic data
Requirements: Master’s degree in health services research, health policy, economics, quantitative psychology, epidemiology, biostatistics, bioinformatics, computer science, computational biology or related field with 2 years of relevant experience with bioinformatic packages is required; Completion of an intense data boot camp is equivalent to 1 year of experience; Experience programming in Stata, R, Python, SAS or some other statistical software required

Duties: The Vice Chair for Clinical Machine Learning (VC - CML) develops and operationalizes Moffitt’s strategic roadmap for the utilization of Artificial Intelligence/Machine Learning (AI/ML) in clinical settings, including the assessment and prioritization of commercial products and research prototypes of developed machine learning algorithms for oncology (e.g., EHR, radiology, pathology, clinical trials, or radiation oncology). The VC-CML also assists the Chair of Machine Learning with academic leadership activities (e.g., recruitment, mentoring, retention, budgeting, strategic planning)
Requirements: PhD and/or MD with expertise in the application of AI/ML to clinical workflows and processes; Technical AI/ML knowledge and skills ideally obtained through formal training in AI and ML, with demonstrated track record in the field; Experience optimizing clinical workflow and/or implementing clinical decision support tools that leverage AI/ML; documented examples of significant work in this area is preferred; Excellent interpersonal communication skills; ability to build team consensus with diverse groups of stakeholders; leads through positive influence; Experience obtaining NIH-grants funding as an independent PI

Duties: Lead end-to-end data science Enterprise Excellence projects, from problem formulation to model deployment to address Citi Line of Business challenges and opportunities; Collaborate closely with cross-functional teams, including technology, re-engineering, product management, and business stakeholders, to define project requirements and deliverables; Apply advanced statistical and machine learning techniques to analyze large, complex datasets and extract actionable insights; Apply mathematical, problem-solving, and coding skills to manage big data, extracting valuable insights; Develop predictive models, algorithms, and data-driven solutions to optimize business process, improve product performance, and drive strategic decision-making; Design and conduct experiments, tests, and casual inference analysis to evaluate the effectiveness of interventions and initiatives
Requirements: Bachelor’s or advanced degree in Computer Science, Statistics, Mathematics, or related field; 6 to 8 years of relevant experience, with 4+ years of professional experience in data science, with strong foundation in statistical analysis, machine learning, and data visualization; Proficiency in programing languages such as Python, R, or Scala, and familiarity with libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, or spark; Solid understanding of experimental design, hypothesis testing, and casual inference methods; Experience with SQL and relational databases for data manipulation and querying; Proficiency with data mining, mathematics, and statistical analysis

Duties: Prepare and validate data, and then build and validate a scoring model using logistic regression or alternative quantitative/machine learning methods; Conduct data-driven analysis and create reports. Apply quantitative and qualitative data analysis methods; prepare statistical and non-statistical data exploration. Validate data, identify data quality issues (if any), and work with Technology to address them. Analyze and interpret data reports, draw conclusions and make recommendations answering specific business needs; Document solutions and present results in a simple comprehensive way to non-technical audience, as well as write more formal documentation using statistical vocabulary; Generate new ideas, concepts and models to improve methods of obtaining and evaluating quantitative and qualitative data. Identify relationships and trends in data, as well as any factors that could affect the results of research. Question and validate assumptions. Escalate identified risks and sensitive ...
Requirements: 3+ Years of relevant experience in strong statistical academic background, knowledge of quantitative methods, including predictive modeling techniques; Experience in creating and assessing Statistical Models; Highly-skilled and good working knowledge of Python/SAS & MS Office Suite; Experience in reporting the results of analysis in clear written form, and in presenting the findings during meetings and conference calls; Team working experience (demonstrated team player ability required)

Duties: Provide strong leadership, mentorship of a large and diverse department of faculty and staff; Provide scientific direction/oversight of the Biostatistics and Bioinformatics Shared Resource; Build relationships with faculty and staff both in the Biostatistics and Bioinformatics Department and across the Moffitt Cancer Center to promote a collaborative environment
Requirements: Successful candidates must have a Ph.D. in statistics, biostatistics or related field and relevant research training and experience (currently Associate- or Full Professor-equivalent rank); Preference will be given to applicants with an outstanding record doing team science or collaborative data science, specifically in cancer

Duties: At Moffitt Cancer Center, we come face-to-face with cancer every day, but we also see courage. And it inspires us to be the safest and best place for cancer care; to bring greater hope to every patient we serve
Requirements: 5 years of experience with Master's Degree or 2 years of experience with PhD in bioinformatics with prior experience in the processing and analysis of next generation DNA sequencing, microarray, comparative genomics, or other high-volume genomics data is required

Duties: Develop and extend machine learning methods for big cancer Omics data such as single-cell omics data and spatial transcriptomics data; Develop bioinformatics pipelines for analyzing high dimensional immunological data; Develop novel visualization tools for interpreting big cancer Omics data; Discover new predictive biomarkers for cancer prognosis; Collaborate on a variety of cancer and bioinformatics research projects
Requirements: Research background/experiences in Bioinformatics, Statistics, Computational Biology, Machine Learning, or Computer Science; A highly motivated and independent researcher with a strong quantitative scientific background; Programming experience in statistical program R and/or scripting languages such as Perl/Python on Unix/Linux systems; Experience in cancer genomics is not required but the ability to learn new knowledge is highly desirable; Excellent communication and writing skills; Experience of integration of multi-omics data such as WES, RNA-seq, metabolomics, proteomics, etc, is preferred

Duties: Conduct independent and collaborative genomics-based research in bioinformatics, particularly in the area of spatial and single cell genomics; Team science collaboration to tackle a variety of cancer clinical and basic science research projects; Communicate clearly and openly; Build relationships to promote a collaborative environment
Requirements: Successful candidates must have a Ph.D. in biomedical informatics, bioinformatics, computational immunology, computer science, biostatistics or related field and relevant research training and experience

Duties: Manage a diverse portfolio of software and databases, ensuring data integrity and accessibility for ongoing institutional research efforts; Provide computational support for scientific projects, including data processing, analysis, and visualization; Collaborate with cross-functional teams to design, implement, and support novel software tools, web applications, and database systems for scientific data analyses; Develop automated software tools to facilitate generic workflow capabilities of scientific data management and ensure easy access to databases; Work closely with researchers and the core team to define and implement new software tools to support cancer research; Will function as a software engineer working closely with core team members and investigators to define and implement new software tools to support cancer research; Train core staff on the use of tools to interface and query database systems
Requirements: Master's Degree in Computer Science, Computer Engineering, Bioinformatics, or a closely related area; 2+ years of experience in scientific software and large-scale database system design and implementation; Strong proficiency in UNIX/LINUX programming and in at least one of the following programming languages: R, Python, C, C++, PHP, Perl, or Java; Ability to translate business requirements into technical specifications and complex datasets into simple recommendations; Git-based version control experience

Duties: Conduct independent and collaborative genomics-based research in bioinformatics, particularly in the area of spatial and single cell genomics; Team science collaboration to tackle a variety of cancer clinical and basic science research projects; Communicate clearly and openly; Build relationships to promote a collaborative environment
Requirements: Successful candidates must have a Ph.D. in biomedical informatics, bioinformatics, computational immunology, computer science, biostatistics or related field and relevant research training and experience

Duties: We are hiring an Applied Bioinformatics Research Scientist to identify metabolic vulnerabilities in lung cancer for therapeutic targeting. Lung Cancer is the most common cause of cancer deaths worldwide. Tyrosine kinase inhibitors and immunotherapy have been shown to be effective in a subset of patients; however, the overall survival rate for this disease remains low, especially for metastatic disease. Moreover, small cell lung cancer (SCLC) patients have a poor prognosis, and there especially exists a gap in knowledge in understanding SCLC and identifying effective therapeutic strategies. The goal of our research team is to understand the underlying biology of key drivers in lung cancer by identifying metabolic vulnerabilities that can ultimately be used as single agents or combined with immunotherapy to target lung cancer therapeutically. Join our team of researchers who are funded through a P01 Program Project Grant!
Requirements: PhD in Bioinformatics, Data Science, Computational Biology, Machine Learning, Statistics/Biostatistics, or related fields; Experience working with at least one “omics” type (e.g., DNA-seq, RNA-seq, metabolomics, proteomics); Preference will be given to candidates with experience in integration of multi-omics data