Dear Students,
new Internship positions are now available.
The projects are related to "Mathematical Models and Methods for Challenging Data Science Applications".
The internships will be carried out in collaboration with the Italian Institute of Technology (IIT) in Genova.
Below are some details related to the available projects.
Duration: 4/6 months.
Fundings: guaranteed by the host institution (IIT).
For further info write an email to rinaldi@math.unipd.it (Please attach a cv and transcript of records to the email you send).
Cheers,
Francesco Rinaldi
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Topic 1: Chromatin Imaging Data Analysis: detection
This research project is related to the extension and application of our
python library Chromatin IMaging Analysis tool (CIMA, unpublished),
aimed at automatic processing and analysis of chromatin imaging data.
Specifically, the project will focus on point-cloud data as thus
obtained with Single-molecule localization microscopy (SMLM) experiments
as OligoSTORM (Nir*,Farabella* et al. PlosGen 2018), aiming at
developing and testing automated methods for the identification and
decoding of imaged chromatin loci. The project will require the
development and analysis of varied density-based (e.g., DSCAN and
HDBSCAN) clustering and network-based community identification methods
(e.g. Louvain method) that have been tested for signal detection
(Piacere et, al. in preparation), use and development of hyperparameters
optimisation tools, coding the algorithmic implementation, and analysis
of both synthetic and real data
Topic 2: Chromatin Imaging Data Analysis: classification
This research project is related to the extension and application of our
python library Chromatin IMaging Analysis tool (CIMA, unpublished),
aimed at automatic processing and analysis of chromatin imaging data.
Specifically, the project will focus on point-cloud data as thus
obtained with Single-molecule localization microscopy (SMLM) experiments
as OligoSTORM (Nir*,Farabella* et al. PlosGen 2018), aiming at
implementing classification methods of imaged chromatin loci. The
project will require the testing of representation methods for 3D points
cloud object, 3D object description, the implementation of 3D alignment
strategies for 3D object comparison, dimensionality reduction ( UMAP,
PACMAP), testing varied classifiers to define chromatin loci sub-types,
and coding the algorithmic implementation, testing it on of both
synthetic and real data.
Topic 3: Computational Genomics
This research project will focus on studying statistical preferences of
long non-coding RNAs in binding to the genome (Farabella et al. Nat.
Struct. Mol. Biol. 2021; Morf et al. Nat Biotechnol. 2019) via
triplex-formation. Specifically, the project aims at acquiring a
multi-omics view of the lncRNA-chromatin interactome, integrating
bioinformatic predictions, RADICL-seq and publicly available
conformation capture experiment. The creation of this common framework
will serve as the starting point to investigate changes in the network
of interaction between lncRNAs and the chromatin (lncRNA-chromatin
interactome) during neural differentiation, especially focusing on
genomic location linked with neurodevelopmental disorders. The project
is part of the FANTOM6 collaborativeefforts, a worldwide collaborative
project aiming at identifying all functional elements in mammalian
genomes.