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Weekend Machine Learning Postdoc information

What is the difference between Weekend Machine Learning Postdoc vs Weekend Data Scientist?

AspectWeekend Machine Learning PostdocWeekend Data Scientist
Required CredentialsPhD in Computer Science, Machine Learning, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field
Work EnvironmentAcademic research settings, universities, research labsIndustry companies, startups, consulting firms
Employer & Industry UsageResearch institutions, universities, academic grantsTech companies, finance, healthcare, retail
Common Search & ComparisonYesYes

The Weekend Machine Learning Postdoc typically involves academic research with a focus on advancing machine learning theories and models, often requiring a PhD. In contrast, a Weekend Data Scientist applies data analysis and machine learning techniques in industry settings, often with a bachelor's or master's degree. Both roles may work on similar projects but differ mainly in their environment, credentials, and end goals.

What are the typical projects and collaboration opportunities for a Weekend Machine Learning Postdoc?

As a Weekend Machine Learning Postdoc, you will often contribute to ongoing research projects, developing and refining machine learning models in collaboration with faculty, graduate students, and occasionally industry partners. While your hours are concentrated on weekends, you’ll typically participate in regular research meetings, code reviews, and may co-author papers or grant proposals. The role provides opportunities to mentor junior researchers and expand your expertise by working on interdisciplinary teams. This structure allows you to make significant research contributions while maintaining flexibility in your schedule.

What is a Weekend Machine Learning Postdoc?

A Weekend Machine Learning Postdoc is a postdoctoral researcher who focuses on machine learning projects and typically works on weekends or has a flexible schedule that includes weekend hours. This role often involves conducting advanced research in machine learning, developing algorithms, publishing papers, and collaborating with academic or industry teams. Weekend postdoc positions may be ideal for those balancing other commitments or seeking non-traditional work hours while continuing their research careers.

What are the key skills and qualifications needed to thrive as a Weekend Machine Learning Postdoc, and why are they important?

To thrive as a Weekend Machine Learning Postdoc, you need a strong background in machine learning, statistics, and programming, typically supported by a PhD in a relevant field. Experience with tools such as Python, TensorFlow, PyTorch, and data analysis platforms, as well as familiarity with academic research methodologies, is essential. Exceptional problem-solving abilities, self-motivation, and effective communication are vital soft skills for success in research and collaboration. These skills enable you to drive innovative research, efficiently manage independent projects, and contribute meaningful insights to the field.
What are the most commonly searched types of Machine Learning Postdoc jobs in Texas? The most popular types of Machine Learning Postdoc jobs in Texas are:
What are popular job titles related to Weekend Machine Learning Postdoc jobs in Texas? For Weekend Machine Learning Postdoc jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Weekend Machine Learning Postdoc jobs in Texas look for? The top searched job categories for Weekend Machine Learning Postdoc jobs in Texas are:
What cities in Texas are hiring for Weekend Machine Learning Postdoc jobs? Cities in Texas with the most Weekend Machine Learning Postdoc job openings:
Weekend Machine Learning Postdoc jobs near you
Postdoctoral Fellow - Biostatistics

Postdoctoral Fellow - Biostatistics

MD Anderson

Houston, TX

$64K - $76K/yr

Full-time

Medical, Dental, Retirement, PTO

Re-posted 6 days ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 169 frontline employees who took The Breakroom Quiz

28th of 881 rated healthcare providers

Job description

The Department of Biostatistics at has one postdoctoral fellow position open for biostatistics and data science methodology research in clinical trials. The main focus is research and publication. The primary focus will be to develop novel methods for causal AI/inference methods, adaptive Bayesian clinical trial designs, derive related statistical theory, produce software for implementation, incorporate biomarkers in clinical trial design and analysis, and build statistical learning tools for large data sets. The postdoctoral fellow will work under the supervision of Dr. Liang on challenging and important clinical and biological projects that involve complex statistical modeling, data analysis, and computation.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
Trainee will learn through various research projects, with a primary focus on: (1) developing novel statistical and data science methods, as well as user-friendly software, for integrating AI tools to evaluate novel treatments or design future clinical trials in overall population or subgroups, and (2) analyzing real-world and institutional medical datasets. A major methodological focus will be integrating machine learning/artificial intelligence tools, causal inference methods, Bayesian techniques, and adaptive designs to build innovative, next-generation tools for adaptively and efficiently evaluating treatment effectiveness and learning optimal treatment decisions that may vary by different patients' subgroups.
ELIGIBILITY REQUIREMENTS
Applicants must have a recent PhD in biostatistics or statistics from a reputed University/Institute or within 0-1 years of graduation. At least one first author publication in a peer reviewed journal stemming from PhD studies is required. Candidates must have strong methodological training in statistics or biostatistics, especially in causal inference or semiparametric methods, and have strong computer programming skills, in particular using R or Python. Expertise or skills in the following areas are highly desirable: Causal inference, double/debias machine learning, semiparametric methods, Bayesian MCMC computational methods, adaptive clinical trials, and machine learning for estimation or decision-making.
Please send CV and information on three referees directly to mliang2@mdanderson.org.
POSITION INFORMATION
MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition
Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

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