Astrobiology Introductory Course (RED) Rencontres Exobiologiques pour Doctorants
PhD thesis offer on biogeomorphological signatures of life in rocks on Earth and Mars
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Detecting biogeomorphological signatures of life in rocks on Earth and Mars
Organisation/Company
Université Clermont Auvergne
Department
Institut Sciences de la vie, Santé, Agronomie, Environnement
Research Field
Earth science, Geology
Application Deadline
28/04/2023 – 17:00 (Europe/Paris) – contact to apply: dov.corenblit@uca.fr
Offer Description
Among the biogenic landforms involving microorganisms that are potentially relevant facets of biosignatures on Mars, microbially induced sedimentary structures (MISS) are promising candidates (Noffke, 2010, 2021). They originate from the interaction between microbial mats constituted by microbe consortiums and their extracellular polymeric substances (EPSs) and sediment dynamics in the intertidal habitats of fluvial, marine, lacustrine, and hypersaline environments, e.g. sabkhas. They are widespread on the Earth in various environments at the interface between water and land. They form rapidly (few days to weeks) and, under adequate conditions of sedimentation they can remain fossilised for billions of years in rock records (Davies et al., 2017; Davies and Shillito, 2021). Microbial mats include bacteria, archaea, protozoans, algae, and fungi leading to the formation of various features from millimetre to decametre scales such as knobby surfaces, sieve-like surfaces, laminar structures, wrinkles, mat chips, palimpsest ripples, roll-up, gas domes, and mat cracks (Noffke, 2010; Davies et al., 2016). Of particular note are mat cracks, which will form the focus of these study. These features have clear abiotic analogues in the crack patterns formed abiotically in drying mud, so the development of the first robust criteria to distinguish biotic and abiotic mud cracks is both readily facilitated by natural examples (modern and ancient), and has immediate utility for distinguishing such in instances of ancient rock record cases and from new rover imagery from Mars.
Detailed understanding of MISS formation on sediment and preservation in rocks is crucial for searching signatures of early life on Earth and Mars. Expert knowledge of MISS structures that are well known from their visual aspect to have a biotic origin, or were at least a strong suspicion exist that they are generated by microorganisms, thus are relevant for searching life signature on Mars (Cady et al., 2004; McLoughlin et al., 2007; Noffke, 2010; Davies et al., 2016). Such method is based on reasoning via abductive inference with the assumption that the same, or equivalent, biogeomorphological processes result in landforms displaying similar characteristics.
Geomorphological visual descriptors on images of the Martian rovers (and potentially orbiters) offer a great potential for the search for biosignatures. Optimizing the use of images for searching life signatures requires the development of adapted and standardized conceptual frameworks and methods (Davies et al., 2016; Noffke, 2018). When the data of an image have adjacency structures that can be caught by the human brain, artificial Neural Network can use these structures by emphasizing local relationships between areas of interests. Neural Network analyses are now recognized as extremely performant in object recognition and classification on images, even of poor definition (LeCun et al., 2015; Zhao et al., 2019). Such techniques have been successfully used in geology and geomorphology for automatic mineral and landform recognition (Li et al., 2020) and in biology for biofilm characterisation (Dimauro et al., 2020).
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
The objectives of the present ExoBioGeo PhD project are largely based on preliminary results obtained since 2018 by the international research team from different laboratories in France and from the University of Cambridge and the University of Oxford, UK, published in the journal Astrobiology (Corenblit et al. 2019; in press). They are also linked to the start of a collaboration with the team of the Institut de Recherche en Astrophysique et Planétologie (IRAP, Toulouse) which controls the ChemCam and SuperCam cameras of the Mars rovers Curiosity and Perseverance. The student will join the camera control team during his PhD. The main objective is to gain a better understanding of MISS formation, preservation, and variability across environmental and biological gradients and to develop and test the best fitting method for the automatic detection of images which represent the most potential to recognize MISS-like structures on the surface of Mars.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
The PhD Project Is Structured Into Tow Tasks (T)
- T1: Field observations of present and ancient MISS for (i) establishing an expert classification of MISS types; and (ii) for constituting a database of images of present and ancient MISS that can be used with Neural Network. The biogeomorphological analyses of ancient MISS will be coupled with geochemical analyses of rock samples at the lab in order to study the relation between geomorphological and chemical parameters.
- T2: Development of the ExoBioGeo adapted Neural Network algorithm (NNa) for the automatic detection of MISS-like structures on images from the field. The potential of this algorithm for use with Martian rover images will be assessed. Special attention will be given on image pre-processing, image segmentation, and multi-scale detection approaches.
Cited References
- Cady, S.L., Farmer, J.D., Grotzinger, J.P., et al. (2004) Morphological biosignatures and the search for life on Mars. Astrobiology 3:351–368.
- Corenblit, D., Darrozes, J., Julien, F., Otto, T., Roussel, E., Steiger, J., and Viles, H. (2019). The search for a signature of life on Mars: a biogeomorphological approach. Astrobiology 19:1279-1291.
- Corenblit, D., Decaux, O., Delmotte, S., Toumazet, J.-P., Arrignon, F., André, M.-F., Darrozes, J., Davies, N., Julien, F., Otto, T., Ramillien, G., Roussel, E., Steiger, J. and Viles, H. (in press). Signatures of life detected in images of rocks using Neural Network analysis demonstrate new potential for searching for biosignatures on the surface of Mars. Astrobiology: DOI: 10.1089/ast.2022.0034
- Davies, N.S., and Shillito, A.P. (2021) True substrates: The exceptional resolution and unexceptional preservation of deep time snapshots on bedding surfaces. Sedimentology 68:3307-3356.
- Davies, N.S., Liu, A.G., Gibling, M.R., et al. (2016) Resolving MISS conceptions and misconceptions: a geological approach to sedimentary surface textures generated by microbial and abiotic processes. Earth-Science Reviews 154:210-246.
- Davies, N.S., Shillito, A.P. and McMahon, W.J. (2017) Short-term evolution of primary sedimentary surface textures (microbial, abiotic, ichnological) on a dry stream bed: modern observations and ancient implications. Palaios 32:125-134
- Dimauro, G., Deperte, F., Maglietta, R., et al. (2020) A Novel Approach for Biofilm Detection Based on a Convolutional Neural Network. Electronics 9:881.
- LeCun, Y., Bengio, Y., and Hinton, G. (2015) Deep learning. Nature 521:436-444.
- Li, S., Xiong, L., Tang, G., et al. (2020) Deep learning-based approach for landform classification from integrated data sources of digital elevation model and imagery. Geomorphology 354:107045.
- McMahon, W.J., Davies, N.S., Liu, A.G. and Went, D.J. (2022) Enigma variations: characteristics and likely origin of the problematic surface texture Arumberia, as recognized from an exceptional bedding plane exposure and the global record. Geological Magazine 159:1-20.
- Noffke, N. (2010) Microbial Mats in Sandy Deposits from the Archean Era to Today. Heidelberg, Germany: Springer Verlag.
- Noffke, N. (2018). Comment on the paper by Davies et al. “Resolving MISS conceptions and misconceptions: A geological approach to sedimentary surface textures generated by microbial and abiotic processes”(Earth Science Reviews, 154 (2016), 210–246). Earth-Science Reviews 176:373-383.
- Noffke, N. (2021) Microbially induced sedimentary structures in clastic deposits: implication for the prospection for fossil life on Mars. Astrobiology 21:866-892.
- Zhao, Z.Q
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills