We are actively recruiting TWO PhD students via the Joint Doctoral Program in Evolutionary Biology at SDSU (collaboratively offered with University of California Riverside), and TWO Masters students via the Biological and Medical Informatics Graduate Program at SDSU, both starting Fall 2022.
Dr. Sethuraman will work with all graduate students to develop their project(s) which can broadly span the fields of genomics, bioinformatics, and evolutionary biology.
Please write to Dr. Sethuraman (firstname.lastname@example.org) with a copy of your CV/Resume, a brief statement of interest, and let’s chat more.
We are committed to recruiting a diverse group of scientists to join my lab group – so we highly encourage folks who identify as a part of historically underrepresented groups to apply. This includes (non-exhaustively) people of color, international students, Veterans of armed forces, student-parents/caregivers, first-generation degree holders, the LGBTQIA+ family, and folks with medical conditions and disabilities.
Description of Doctoral positions
Start Date: Fall 2022
Application Deadline: December 15, 2021
A Bachelors or Masters degree in Biology/Bioinformatics/Genetics/Computer Science/Statistics, or a related field. Knowledge and experience in scientific programming using C/C++/Python is highly encouraged, but not necessary.
As of 2020, the GRE is no longer required for admissions to the program, but international students are required to submit scores from the TOEFL. More information about admission requirements and procedures can be found at: http://www.bio.sdsu.edu/eb/jdapplications.html
Position 1: Funded by NSF CAREER #2042516 to PI Sethuraman
We currently have an opening for a PhD student (with research funding for ~5 years) to work on projects developing new statistical methods, software, and pipelines for estimation of population structure, relatedness, and evolutionary history from large scale population genomic data while accounting for missingness. Specifically, we will address missingness due to (1) genotyping/variant calling errors, (2) allele dropout in genotyping by sequencing methods – e.g. RADseq, and (3) gene flow from unsampled “ghost” populations.
Position 2: Funded by NIH R15 #1R15GM143700-01 to PI Sethuraman
This PhD student (with research funding for ~5 years) will work on projects in human population genomics, focused on developing new statistical methods, software, and pipelines for analyses of large scale human genomic data to (1) accurately estimate population structure in the presence of archaic gene flow, (2) analyze large human genomic datasets to quantify archaic variation, and (3) understanding patterns of natural selection and adaptation at introgressed archaic variants and haplotypes across humans.
Description of Masters positions
Start Date: Fall 2022, and subsequent Fall terms
Application Deadline: March 1, 2022
A Bachelors degree in Biology/Bioinformatics/Genetics/Computer Science/Statistics, or a related field. As of 2020, the GRE is no longer required for admissions to the BMI program. More information about admission requirements and procedures can be found at: https://informatics.sdsu.edu/admissions/.
We are looking to recruit TWO enthusiastic Masters students to work on various population genomics projects.
Current “Wet” Lab Projects
1. Population genomics of invasive lady beetles (Coleoptera: Coccinellidae)
2. Evolutionary history of domestication of hops (Humulus lupulus)
Current “Dry” Lab Projects (Data Analyses)
3. Annotation, phylogenomic history using the genome of the convergent lady beetle (Hippodamia convergens)
4. Population genomics of a parthenogenetic parasitoid wasp, Dinocampus coccinellae using Ultra Conserved Elements
5. Population genomics of several other species of lady beetles (Coleoptera: Coccinellidae), including Hippodamia convergens, Harmonia axyridis, Propylea quatrodecimpunctata, and Hippodamia variegata
6. Identifying and understanding the evolutionary history in regions of modern human genomes that are of “archaic” (e.g. Neanderthal) ancestry
Current “Dry” Lab Projects (Software Development/Testing)
7. Testing of PPP – the Pop-Gen Pipeline Platform, developing Jupyter Notebooks on publicly available RADseq datasets (Python)
8. Development, deployment, testing of a PPP GUI via The Galaxy Project (usegalaxy.org) (Python/XML)
9. Testing p-MULTICLUST, a parallelized method for estimating population structure (C, Python)
10. Developing a new software for estimating genetic relatedness (C/C++/Python)
Come join us!