develop novel algorithms and leverage all available DNA and protein sequence bioinformatics, immunopeptidomics, artificial intelligence tools and algorithms to identify key personalized neoantigens. 1.Utilize state-of-the-art sequence-data mining technologies to process and analyze large sequences dataset of whole-exome sequencing (WES) and RNA sequencing (RNA-seq), to identify somatic mutations, RNA expression, and human leukocyte antigen (HLA) alleles. 2.Use available and emerging state-of-the-art tools and AI-driven algorithms to identify potential neoantigen peptides, to predict HLA-I binding affinity, and to prioritize the peptides. 3.Developing and implementing a set of best practices to predict high-quality immunogenic neoantigens for better personalized neoantigen cancer vaccine. 4.Develop and apply novel combinatorial optimization algorithms to solve the needs and challenges specific to neoantigen prediction, continue formalizing, streamlining, and improving the neoantigen identification process.

1.PhD in Bioinformatics, Computational Biology, with 1+ years of related experience, or MS in Bioinformatics with 4+ years of related experience. 2.Proficient in next generation sequencing (WES sequencing and RNASeq) data analysis, including data quality assessment, sequencing data assembling, mapping, mutation and structural variation analysis, transcript profiling, HLA typing, and splicing analysis. 3.Familiar with commonly used variant-calling software, such as Deep Variant, GATK MuTect2, and VarScan2, etc 4.Experience in prediction of neoantigen peptides, familiar with common tools such as NetChop, Proteasome Cleavage Prediction Server (PCPS), and NetCTL, etc 5.Experience in modeling and predicting peptide-HLA-I binding, familiar with tools and algorithms such as NetMHCpan4.1, MHCflurry2.0, ConvMHC, and PAComplex, etc 6.Expertise in tools and algorithms for MHC-I neoantigen prioritization models，such as pVAC-Seq, TSNAD, CloudNeo, TIminer, MuPeXI, INTEGRATE-Neo, NeoantigenR, HLAthena, EDGE, and DeepHLApan. 7.Expertise in tools for the prediction of T cells epitopes, such as motif-based system, matrix, SVM, empirical scoring, Machine learning algorithms (MLAs), and molecular dynamics (MDs) methods. 8.Familiar with publicly available immunopeptidome databases, such as IEDB and CEDAR, etc