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Below information is for incoming or existing biostatistics students interested in doing PhD thesis in genomic research in our group.
Prospective students not yet admitted to the department: Our group usually recruits PhD students from the Biostatistics department applicants every year. If you have not entered Pitt yet, please apply through the department using the normal application track. If a GSR funding comes up, I usually look into the applicant pool in spring to identify an adequate student. I sometimes also recruit students from the Joint CMU-Pit PhD Program in Computational Biology. I usually recruit PhD students from their first year so they start early training and early publications. When they graduate, their track record is competitive for job hunting.
Existing students in Biostatistics: We occasionally recruit high-performing existing students for GSR when funding allows. For interested students, it is recommended to take the genomic data analysis course sequence we offer every spring and fall.
Career for Biostatistics PhD students with genomic research training:
I'm often asked about biostatistics career options when students are close to graduate. In my opinion, students should start to think about their life vision and career planning when they start their PhD study although the decision may change when they know themselves better during the PhD training. Below I try my best to list potential career routes for a Biostatistics PhD with thesis on statistical genomics/bioinformatics research. This is certainly not exhaustive but only to the best of my knowledge. I hope this gives young students better vision and deeper thinking on what fits you best in your career.
1. Academic research positions:
Genomics is an expanding and fast moving field. There're increasing number of research positions in this field, which ties closely to the modern statistical learning and data science (big data) field. To manage and analyze the large databases and high-throughput experimental data, statistics plays an important role in this field. In general, the advantages of working in academic include more challenging/exciting work content, more flexible working schedules and more transparent promotion procedures.
(a) Tenure track position: If you’re very interested in doing research and teaching, this is the choice. It has greater challenges and pressure but also gives much greater freedom and fun without a real boss above you. If you are talented, bored of doing routine things and feel like flexible working hours, you should seriously consider a career towards it.
(b) Postdoc position => tenure track position: It is certainly competitive to obtain a tenure track position right after PhD. A better and less pressure alternative is to find a good reputation research group in top schools and do a 2-3 year postdoc. You will sacrifice some salary income during this period (although NIH has raised postdoc salary to a reasonable amount now) but you will gain enough publications to get a tenure track position in a higher-ranking university to start with (the research environment is very important to biostatisticians as we work closely with collaborators). A postdoc training also gives time for better planning of your research career and the postdoc compensation for biostatisticians is usually not bad compared to traditional biological scientists.
(c) Research track position: There are many research track positions to work in divisions/centers or big research groups in medical school. The position is not permanent but if the group is stable enough, it’s almost permanent. The challenges and freedom is usually in between a tenure track position and an industry job, depending on the work content and PI of the group.
2. Industry:
Traditional biostatisticians are trained with focus in clinical trial, survival analysis, longitudianl data, linear/mixed models, categorical data and etc. With the increasing importance of genomics in drug development and medical research, we've seen increasing needs for biostatisticians with genomic training. In general, industry jobs are more routine and the promotion usually more depends on the company culture and direct supervisor above you etc (it's more difficult to judge working performance in industry). There are also less freedom in work content and research direction as the company's goal is to make money. Many industry jobs are suitable for people who enjoy or want a job with routine work for salary (which is not bad if this is really what you want). But I encourage young students to think carefully the downside of industry jobs as IÕve seen many talented students get bored in industry jobs and try to return to research positions (which wastes time and has become very difficult these days).
(a) pre-clinical (Phase I or II) or R&D departments: These jobs are more challenging than other industrial jobs and require high communication skills to work in a team with many chemists and biologists. It is sometimes frustrating since only an averge of one out of one thousand drug targets will eventually work but it's certainly the foundation of drug development.
(b) phase III or IV clinical trial: These jobs are considered more routine and can be boring as most things you do are to get the drug approved by FDA.
(c) consulting company: This is an expanding industry. More and more big companies and government agencies have slowed down expansion of their statistical units and rely on these consulting companies. These jobs require fast learning skills, good communication and maybe a lot of travelling.
(d) biotech (start-up) company: Usually more challenging (and exciting) and work content may be variable.
3. Government:
Similar to industry, there are increasing needs for biostatisticians with genomic training in many government agencies including NIH, FDA, CDC and VA system. These positions usually require US citizenship (or green card).
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